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Type 'q()' to quit R. > #data <- read.table("E:\\ANN_GLM_SVM\\ANOVA\\anova7_regression.txt", sep = "\t", header = T) > > options(warn=-1) > options(error=recover) > > > data <- read.table("anova7_regression.txt", sep = "\t", header = T) > datasubset <- data[c(1:150),1:3] > > datanoANN <- datasubset[c(11:30,41:60,71:90,101:120,131:150),1:3] > datanoGLM <- datasubset[c(1:10,21:40,51:70,81:100,111:130,141:150),1:3] > datanoSVM <- datasubset[c(1:20,31:50,61:80,91:110,121:140),1:3] > > datanoPSBC <- datasubset[c(31:150),1:3] > datanoTHER <- datasubset[c(1:30,61:150),1:3] > datanoNG25 <- datasubset[c(1:60,91:150),1:3] > datanoGSSF <- datasubset[c(1:90,121:150),1:3] > datanoGSSS <- datasubset[c(1:120),1:3] > > > AA <- data$outcome[c(1:10)] > BA <- data$outcome[c(11:20)] > CA <- data$outcome[c(21:30)] > AB <- data$outcome[c(31:40)] > BB <- data$outcome[c(41:50)] > CB <- data$outcome[c(51:60)] > AC <- data$outcome[c(61:70)] > BC <- data$outcome[c(71:80)] > CC <- data$outcome[c(81:90)] > AD <- data$outcome[c(91:100)] > BD <- data$outcome[c(101:110)] > CD <- data$outcome[c(111:120)] > AE <- data$outcome[c(121:130)] > BE <- data$outcome[c(131:140)] > CE <- data$outcome[c(141:150)] > AF <- data$outcome[c(151:160)] > BF <- data$outcome[c(161:170)] > CF <- data$outcome[c(171:180)] > AG <- data$outcome[c(181:190)] > BG <- data$outcome[c(191:200)] > CG <- data$outcome[c(201:210)] > AH <- data$outcome[c(211:220)] > BH <- data$outcome[c(221:230)] > CH <- data$outcome[c(231:240)] > > AVEAAAA <- mean((AA+AA)/2) > AVEAAAB <- mean((AA+AB)/2) > AVEAAAC <- mean((AA+AC)/2) > AVEAAAD <- mean((AA+AD)/2) > AVEAAAE <- mean((AA+AE)/2) > AVEAAAF <- mean((AA+AF)/2) > AVEAAAG <- mean((AA+AG)/2) > AVEAAAH <- mean((AA+AH)/2) > AVEAABA <- mean((AA+BA)/2) > AVEAABB <- mean((AA+BB)/2) > AVEAABC <- mean((AA+BC)/2) > AVEAABD <- mean((AA+BD)/2) > AVEAABE <- mean((AA+BE)/2) > AVEAABF <- mean((AA+BF)/2) > AVEAABG <- mean((AA+BG)/2) > AVEAABH <- mean((AA+BH)/2) > AVEAACA <- mean((AA+CA)/2) > AVEAACB <- mean((AA+CB)/2) > AVEAACC <- mean((AA+CC)/2) > AVEAACD <- mean((AA+CD)/2) > AVEAACE <- mean((AA+CE)/2) > AVEAACF <- mean((AA+CF)/2) > AVEAACG <- mean((AA+CG)/2) > AVEAACH <- mean((AA+CH)/2) > AVEABAA <- mean((AB+AA)/2) > AVEABAB <- mean((AB+AB)/2) > AVEABAC <- mean((AB+AC)/2) > AVEABAD <- mean((AB+AD)/2) > AVEABAE <- mean((AB+AE)/2) > AVEABAF <- mean((AB+AF)/2) > AVEABAG <- mean((AB+AG)/2) > AVEABAH <- mean((AB+AH)/2) > AVEABBA <- mean((AB+BA)/2) > AVEABBB <- mean((AB+BB)/2) > AVEABBC <- mean((AB+BC)/2) > AVEABBD <- mean((AB+BD)/2) > AVEABBE <- mean((AB+BE)/2) > AVEABBF <- mean((AB+BF)/2) > AVEABBG <- mean((AB+BG)/2) > AVEABBH <- mean((AB+BH)/2) > AVEABCA <- mean((AB+CA)/2) > AVEABCB <- mean((AB+CB)/2) > AVEABCC <- mean((AB+CC)/2) > AVEABCD <- mean((AB+CD)/2) > AVEABCE <- mean((AB+CE)/2) > AVEABCF <- mean((AB+CF)/2) > AVEABCG <- mean((AB+CG)/2) > AVEABCH <- mean((AB+CH)/2) > AVEACAA <- mean((AC+AA)/2) > AVEACAB <- mean((AC+AB)/2) > AVEACAC <- mean((AC+AC)/2) > AVEACAD <- mean((AC+AD)/2) > AVEACAE <- mean((AC+AE)/2) > AVEACAF <- mean((AC+AF)/2) > AVEACAG <- mean((AC+AG)/2) > AVEACAH <- mean((AC+AH)/2) > AVEACBA <- mean((AC+BA)/2) > AVEACBB <- mean((AC+BB)/2) > AVEACBC <- mean((AC+BC)/2) > AVEACBD <- mean((AC+BD)/2) > AVEACBE <- mean((AC+BE)/2) > AVEACBF <- mean((AC+BF)/2) > AVEACBG <- mean((AC+BG)/2) > AVEACBH <- mean((AC+BH)/2) > AVEACCA <- mean((AC+CA)/2) > AVEACCB <- mean((AC+CB)/2) > AVEACCC <- mean((AC+CC)/2) > AVEACCD <- mean((AC+CD)/2) > AVEACCE <- mean((AC+CE)/2) > AVEACCF <- mean((AC+CF)/2) > AVEACCG <- mean((AC+CG)/2) > AVEACCH <- mean((AC+CH)/2) > AVEADAA <- mean((AD+AA)/2) > AVEADAB <- mean((AD+AB)/2) > AVEADAC <- mean((AD+AC)/2) > AVEADAD <- mean((AD+AD)/2) > AVEADAE <- mean((AD+AE)/2) > AVEADAF <- mean((AD+AF)/2) > AVEADAG <- mean((AD+AG)/2) > AVEADAH <- mean((AD+AH)/2) > AVEADBA <- mean((AD+BA)/2) > AVEADBB <- mean((AD+BB)/2) > AVEADBC <- mean((AD+BC)/2) > AVEADBD <- mean((AD+BD)/2) > AVEADBE <- mean((AD+BE)/2) > AVEADBF <- mean((AD+BF)/2) > AVEADBG <- mean((AD+BG)/2) > AVEADBH <- mean((AD+BH)/2) > AVEADCA <- mean((AD+CA)/2) > AVEADCB <- mean((AD+CB)/2) > AVEADCC <- mean((AD+CC)/2) > AVEADCD <- mean((AD+CD)/2) > AVEADCE <- mean((AD+CE)/2) > AVEADCF <- mean((AD+CF)/2) > AVEADCG <- mean((AD+CG)/2) > AVEADCH <- mean((AD+CH)/2) > AVEAEAA <- mean((AE+AA)/2) > AVEAEAB <- mean((AE+AB)/2) > AVEAEAC <- mean((AE+AC)/2) > AVEAEAD <- mean((AE+AD)/2) > AVEAEAE <- mean((AE+AE)/2) > AVEAEAF <- mean((AE+AF)/2) > AVEAEAG <- mean((AE+AG)/2) > AVEAEAH <- mean((AE+AH)/2) > AVEAEBA <- mean((AE+BA)/2) > AVEAEBB <- mean((AE+BB)/2) > AVEAEBC <- mean((AE+BC)/2) > AVEAEBD <- mean((AE+BD)/2) > AVEAEBE <- mean((AE+BE)/2) > AVEAEBF <- mean((AE+BF)/2) > AVEAEBG <- mean((AE+BG)/2) > AVEAEBH <- mean((AE+BH)/2) > AVEAECA <- mean((AE+CA)/2) > AVEAECB <- mean((AE+CB)/2) > AVEAECC <- mean((AE+CC)/2) > AVEAECD <- mean((AE+CD)/2) > AVEAECE <- mean((AE+CE)/2) > AVEAECF <- mean((AE+CF)/2) > AVEAECG <- mean((AE+CG)/2) > AVEAECH <- mean((AE+CH)/2) > AVEAFAA <- mean((AF+AA)/2) > AVEAFAB <- mean((AF+AB)/2) > AVEAFAC <- mean((AF+AC)/2) > AVEAFAD <- mean((AF+AD)/2) > AVEAFAE <- mean((AF+AE)/2) > AVEAFAF <- mean((AF+AF)/2) > AVEAFAG <- mean((AF+AG)/2) > AVEAFAH <- mean((AF+AH)/2) > AVEAFBA <- mean((AF+BA)/2) > AVEAFBB <- mean((AF+BB)/2) > AVEAFBC <- mean((AF+BC)/2) > AVEAFBD <- mean((AF+BD)/2) > AVEAFBE <- mean((AF+BE)/2) > AVEAFBF <- mean((AF+BF)/2) > AVEAFBG <- mean((AF+BG)/2) > AVEAFBH <- mean((AF+BH)/2) > AVEAFCA <- mean((AF+CA)/2) > AVEAFCB <- mean((AF+CB)/2) > AVEAFCC <- mean((AF+CC)/2) > AVEAFCD <- mean((AF+CD)/2) > AVEAFCE <- mean((AF+CE)/2) > AVEAFCF <- mean((AF+CF)/2) > AVEAFCG <- mean((AF+CG)/2) > AVEAFCH <- mean((AF+CH)/2) > AVEAGAA <- mean((AG+AA)/2) > AVEAGAB <- mean((AG+AB)/2) > AVEAGAC <- mean((AG+AC)/2) > AVEAGAD <- mean((AG+AD)/2) > AVEAGAE <- mean((AG+AE)/2) > AVEAGAF <- mean((AG+AF)/2) > AVEAGAG <- mean((AG+AG)/2) > AVEAGAH <- mean((AG+AH)/2) > AVEAGBA <- mean((AG+BA)/2) > AVEAGBB <- mean((AG+BB)/2) > AVEAGBC <- mean((AG+BC)/2) > AVEAGBD <- mean((AG+BD)/2) > AVEAGBE <- mean((AG+BE)/2) > AVEAGBF <- mean((AG+BF)/2) > AVEAGBG <- mean((AG+BG)/2) > AVEAGBH <- mean((AG+BH)/2) > AVEAGCA <- mean((AG+CA)/2) > AVEAGCB <- mean((AG+CB)/2) > AVEAGCC <- mean((AG+CC)/2) > AVEAGCD <- mean((AG+CD)/2) > AVEAGCE <- mean((AG+CE)/2) > AVEAGCF <- mean((AG+CF)/2) > AVEAGCG <- mean((AG+CG)/2) > AVEAGCH <- mean((AG+CH)/2) > AVEAHAA <- mean((AH+AA)/2) > AVEAHAB <- mean((AH+AB)/2) > AVEAHAC <- mean((AH+AC)/2) > AVEAHAD <- mean((AH+AD)/2) > AVEAHAE <- mean((AH+AE)/2) > AVEAHAF <- mean((AH+AF)/2) > AVEAHAG <- mean((AH+AG)/2) > AVEAHAH <- mean((AH+AH)/2) > AVEAHBA <- mean((AH+BA)/2) > AVEAHBB <- mean((AH+BB)/2) > AVEAHBC <- mean((AH+BC)/2) > AVEAHBD <- mean((AH+BD)/2) > AVEAHBE <- mean((AH+BE)/2) > AVEAHBF <- mean((AH+BF)/2) > AVEAHBG <- mean((AH+BG)/2) > AVEAHBH <- mean((AH+BH)/2) > AVEAHCA <- mean((AH+CA)/2) > AVEAHCB <- mean((AH+CB)/2) > AVEAHCC <- mean((AH+CC)/2) > AVEAHCD <- mean((AH+CD)/2) > AVEAHCE <- mean((AH+CE)/2) > AVEAHCF <- mean((AH+CF)/2) > AVEAHCG <- mean((AH+CG)/2) > AVEAHCH <- mean((AH+CH)/2) > AVEBAAA <- mean((BA+AA)/2) > AVEBAAB <- mean((BA+AB)/2) > AVEBAAC <- mean((BA+AC)/2) > AVEBAAD <- mean((BA+AD)/2) > AVEBAAE <- mean((BA+AE)/2) > AVEBAAF <- mean((BA+AF)/2) > AVEBAAG <- mean((BA+AG)/2) > AVEBAAH <- mean((BA+AH)/2) > AVEBABA <- mean((BA+BA)/2) > AVEBABB <- mean((BA+BB)/2) > AVEBABC <- mean((BA+BC)/2) > AVEBABD <- mean((BA+BD)/2) > AVEBABE <- mean((BA+BE)/2) > AVEBABF <- mean((BA+BF)/2) > AVEBABG <- mean((BA+BG)/2) > AVEBABH <- mean((BA+BH)/2) > AVEBACA <- mean((BA+CA)/2) > AVEBACB <- mean((BA+CB)/2) > AVEBACC <- mean((BA+CC)/2) > AVEBACD <- mean((BA+CD)/2) > AVEBACE <- mean((BA+CE)/2) > AVEBACF <- mean((BA+CF)/2) > AVEBACG <- mean((BA+CG)/2) > AVEBACH <- mean((BA+CH)/2) > AVEBBAA <- mean((BB+AA)/2) > AVEBBAB <- mean((BB+AB)/2) > AVEBBAC <- mean((BB+AC)/2) > AVEBBAD <- mean((BB+AD)/2) > AVEBBAE <- mean((BB+AE)/2) > AVEBBAF <- mean((BB+AF)/2) > AVEBBAG <- mean((BB+AG)/2) > AVEBBAH <- mean((BB+AH)/2) > AVEBBBA <- mean((BB+BA)/2) > AVEBBBB <- mean((BB+BB)/2) > AVEBBBC <- mean((BB+BC)/2) > AVEBBBD <- mean((BB+BD)/2) > AVEBBBE <- mean((BB+BE)/2) > AVEBBBF <- mean((BB+BF)/2) > AVEBBBG <- mean((BB+BG)/2) > AVEBBBH <- mean((BB+BH)/2) > AVEBBCA <- mean((BB+CA)/2) > AVEBBCB <- mean((BB+CB)/2) > AVEBBCC <- mean((BB+CC)/2) > AVEBBCD <- mean((BB+CD)/2) > AVEBBCE <- mean((BB+CE)/2) > AVEBBCF <- mean((BB+CF)/2) > AVEBBCG <- mean((BB+CG)/2) > AVEBBCH <- mean((BB+CH)/2) > AVEBCAA <- mean((BC+AA)/2) > AVEBCAB <- mean((BC+AB)/2) > AVEBCAC <- mean((BC+AC)/2) > AVEBCAD <- mean((BC+AD)/2) > AVEBCAE <- mean((BC+AE)/2) > AVEBCAF <- mean((BC+AF)/2) > AVEBCAG <- mean((BC+AG)/2) > AVEBCAH <- mean((BC+AH)/2) > AVEBCBA <- mean((BC+BA)/2) > AVEBCBB <- mean((BC+BB)/2) > AVEBCBC <- mean((BC+BC)/2) > AVEBCBD <- mean((BC+BD)/2) > AVEBCBE <- mean((BC+BE)/2) > AVEBCBF <- mean((BC+BF)/2) > AVEBCBG <- mean((BC+BG)/2) > AVEBCBH <- mean((BC+BH)/2) > AVEBCCA <- mean((BC+CA)/2) > AVEBCCB <- mean((BC+CB)/2) > AVEBCCC <- mean((BC+CC)/2) > AVEBCCD <- mean((BC+CD)/2) > AVEBCCE <- mean((BC+CE)/2) > AVEBCCF <- mean((BC+CF)/2) > AVEBCCG <- mean((BC+CG)/2) > AVEBCCH <- mean((BC+CH)/2) > AVEBDAA <- mean((BD+AA)/2) > AVEBDAB <- mean((BD+AB)/2) > AVEBDAC <- mean((BD+AC)/2) > AVEBDAD <- mean((BD+AD)/2) > AVEBDAE <- mean((BD+AE)/2) > AVEBDAF <- mean((BD+AF)/2) > AVEBDAG <- mean((BD+AG)/2) > AVEBDAH <- mean((BD+AH)/2) > AVEBDBA <- mean((BD+BA)/2) > AVEBDBB <- mean((BD+BB)/2) > AVEBDBC <- mean((BD+BC)/2) > AVEBDBD <- mean((BD+BD)/2) > AVEBDBE <- mean((BD+BE)/2) > AVEBDBF <- mean((BD+BF)/2) > AVEBDBG <- mean((BD+BG)/2) > AVEBDBH <- mean((BD+BH)/2) > AVEBDCA <- mean((BD+CA)/2) > AVEBDCB <- mean((BD+CB)/2) > AVEBDCC <- mean((BD+CC)/2) > AVEBDCD <- mean((BD+CD)/2) > AVEBDCE <- mean((BD+CE)/2) > AVEBDCF <- mean((BD+CF)/2) > AVEBDCG <- mean((BD+CG)/2) > AVEBDCH <- mean((BD+CH)/2) > AVEBEAA <- mean((BE+AA)/2) > AVEBEAB <- mean((BE+AB)/2) > AVEBEAC <- mean((BE+AC)/2) > AVEBEAD <- mean((BE+AD)/2) > AVEBEAE <- mean((BE+AE)/2) > AVEBEAF <- mean((BE+AF)/2) > AVEBEAG <- mean((BE+AG)/2) > AVEBEAH <- mean((BE+AH)/2) > AVEBEBA <- mean((BE+BA)/2) > AVEBEBB <- mean((BE+BB)/2) > AVEBEBC <- mean((BE+BC)/2) > AVEBEBD <- mean((BE+BD)/2) > AVEBEBE <- mean((BE+BE)/2) > AVEBEBF <- mean((BE+BF)/2) > AVEBEBG <- mean((BE+BG)/2) > AVEBEBH <- mean((BE+BH)/2) > AVEBECA <- mean((BE+CA)/2) > AVEBECB <- mean((BE+CB)/2) > AVEBECC <- mean((BE+CC)/2) > AVEBECD <- mean((BE+CD)/2) > AVEBECE <- mean((BE+CE)/2) > AVEBECF <- mean((BE+CF)/2) > AVEBECG <- mean((BE+CG)/2) > AVEBECH <- mean((BE+CH)/2) > AVEBFAA <- mean((BF+AA)/2) > AVEBFAB <- mean((BF+AB)/2) > AVEBFAC <- mean((BF+AC)/2) > AVEBFAD <- mean((BF+AD)/2) > AVEBFAE <- mean((BF+AE)/2) > AVEBFAF <- mean((BF+AF)/2) > AVEBFAG <- mean((BF+AG)/2) > AVEBFAH <- mean((BF+AH)/2) > AVEBFBA <- mean((BF+BA)/2) > AVEBFBB <- mean((BF+BB)/2) > AVEBFBC <- mean((BF+BC)/2) > AVEBFBD <- mean((BF+BD)/2) > AVEBFBE <- mean((BF+BE)/2) > AVEBFBF <- mean((BF+BF)/2) > AVEBFBG <- mean((BF+BG)/2) > AVEBFBH <- mean((BF+BH)/2) > AVEBFCA <- mean((BF+CA)/2) > AVEBFCB <- mean((BF+CB)/2) > AVEBFCC <- mean((BF+CC)/2) > AVEBFCD <- mean((BF+CD)/2) > AVEBFCE <- mean((BF+CE)/2) > AVEBFCF <- mean((BF+CF)/2) > AVEBFCG <- mean((BF+CG)/2) > AVEBFCH <- mean((BF+CH)/2) > AVEBGAA <- mean((BG+AA)/2) > AVEBGAB <- mean((BG+AB)/2) > AVEBGAC <- mean((BG+AC)/2) > AVEBGAD <- mean((BG+AD)/2) > AVEBGAE <- mean((BG+AE)/2) > AVEBGAF <- mean((BG+AF)/2) > AVEBGAG <- mean((BG+AG)/2) > AVEBGAH <- mean((BG+AH)/2) > AVEBGBA <- mean((BG+BA)/2) > AVEBGBB <- mean((BG+BB)/2) > AVEBGBC <- mean((BG+BC)/2) > AVEBGBD <- mean((BG+BD)/2) > AVEBGBE <- mean((BG+BE)/2) > AVEBGBF <- mean((BG+BF)/2) > AVEBGBG <- mean((BG+BG)/2) > AVEBGBH <- mean((BG+BH)/2) > AVEBGCA <- mean((BG+CA)/2) > AVEBGCB <- mean((BG+CB)/2) > AVEBGCC <- mean((BG+CC)/2) > AVEBGCD <- mean((BG+CD)/2) > AVEBGCE <- mean((BG+CE)/2) > AVEBGCF <- mean((BG+CF)/2) > AVEBGCG <- mean((BG+CG)/2) > AVEBGCH <- mean((BG+CH)/2) > AVEBHAA <- mean((BH+AA)/2) > AVEBHAB <- mean((BH+AB)/2) > AVEBHAC <- mean((BH+AC)/2) > AVEBHAD <- mean((BH+AD)/2) > AVEBHAE <- mean((BH+AE)/2) > AVEBHAF <- mean((BH+AF)/2) > AVEBHAG <- mean((BH+AG)/2) > AVEBHAH <- mean((BH+AH)/2) > AVEBHBA <- mean((BH+BA)/2) > AVEBHBB <- mean((BH+BB)/2) > AVEBHBC <- mean((BH+BC)/2) > AVEBHBD <- mean((BH+BD)/2) > AVEBHBE <- mean((BH+BE)/2) > AVEBHBF <- mean((BH+BF)/2) > AVEBHBG <- mean((BH+BG)/2) > AVEBHBH <- mean((BH+BH)/2) > AVEBHCA <- mean((BH+CA)/2) > AVEBHCB <- mean((BH+CB)/2) > AVEBHCC <- mean((BH+CC)/2) > AVEBHCD <- mean((BH+CD)/2) > AVEBHCE <- mean((BH+CE)/2) > AVEBHCF <- mean((BH+CF)/2) > AVEBHCG <- mean((BH+CG)/2) > AVEBHCH <- mean((BH+CH)/2) > AVECAAA <- mean((CA+AA)/2) > AVECAAB <- mean((CA+AB)/2) > AVECAAC <- mean((CA+AC)/2) > AVECAAD <- mean((CA+AD)/2) > AVECAAE <- mean((CA+AE)/2) > AVECAAF <- mean((CA+AF)/2) > AVECAAG <- mean((CA+AG)/2) > AVECAAH <- mean((CA+AH)/2) > AVECABA <- mean((CA+BA)/2) > AVECABB <- mean((CA+BB)/2) > AVECABC <- mean((CA+BC)/2) > AVECABD <- mean((CA+BD)/2) > AVECABE <- mean((CA+BE)/2) > AVECABF <- mean((CA+BF)/2) > AVECABG <- mean((CA+BG)/2) > AVECABH <- mean((CA+BH)/2) > AVECACA <- mean((CA+CA)/2) > AVECACB <- mean((CA+CB)/2) > AVECACC <- mean((CA+CC)/2) > AVECACD <- mean((CA+CD)/2) > AVECACE <- mean((CA+CE)/2) > AVECACF <- mean((CA+CF)/2) > AVECACG <- mean((CA+CG)/2) > AVECACH <- mean((CA+CH)/2) > AVECBAA <- mean((CB+AA)/2) > AVECBAB <- mean((CB+AB)/2) > AVECBAC <- mean((CB+AC)/2) > AVECBAD <- mean((CB+AD)/2) > AVECBAE <- mean((CB+AE)/2) > AVECBAF <- mean((CB+AF)/2) > AVECBAG <- mean((CB+AG)/2) > AVECBAH <- mean((CB+AH)/2) > AVECBBA <- mean((CB+BA)/2) > AVECBBB <- mean((CB+BB)/2) > AVECBBC <- mean((CB+BC)/2) > AVECBBD <- mean((CB+BD)/2) > AVECBBE <- mean((CB+BE)/2) > AVECBBF <- mean((CB+BF)/2) > AVECBBG <- mean((CB+BG)/2) > AVECBBH <- mean((CB+BH)/2) > AVECBCA <- mean((CB+CA)/2) > AVECBCB <- mean((CB+CB)/2) > AVECBCC <- mean((CB+CC)/2) > AVECBCD <- mean((CB+CD)/2) > AVECBCE <- mean((CB+CE)/2) > AVECBCF <- mean((CB+CF)/2) > AVECBCG <- mean((CB+CG)/2) > AVECBCH <- mean((CB+CH)/2) > AVECCAA <- mean((CC+AA)/2) > AVECCAB <- mean((CC+AB)/2) > AVECCAC <- mean((CC+AC)/2) > AVECCAD <- mean((CC+AD)/2) > AVECCAE <- mean((CC+AE)/2) > AVECCAF <- mean((CC+AF)/2) > AVECCAG <- mean((CC+AG)/2) > AVECCAH <- mean((CC+AH)/2) > AVECCBA <- mean((CC+BA)/2) > AVECCBB <- mean((CC+BB)/2) > AVECCBC <- mean((CC+BC)/2) > AVECCBD <- mean((CC+BD)/2) > AVECCBE <- mean((CC+BE)/2) > AVECCBF <- mean((CC+BF)/2) > AVECCBG <- mean((CC+BG)/2) > AVECCBH <- mean((CC+BH)/2) > AVECCCA <- mean((CC+CA)/2) > AVECCCB <- mean((CC+CB)/2) > AVECCCC <- mean((CC+CC)/2) > AVECCCD <- mean((CC+CD)/2) > AVECCCE <- mean((CC+CE)/2) > AVECCCF <- mean((CC+CF)/2) > AVECCCG <- mean((CC+CG)/2) > AVECCCH <- mean((CC+CH)/2) > AVECDAA <- mean((CD+AA)/2) > AVECDAB <- mean((CD+AB)/2) > AVECDAC <- mean((CD+AC)/2) > AVECDAD <- mean((CD+AD)/2) > AVECDAE <- mean((CD+AE)/2) > AVECDAF <- mean((CD+AF)/2) > AVECDAG <- mean((CD+AG)/2) > AVECDAH <- mean((CD+AH)/2) > AVECDBA <- mean((CD+BA)/2) > AVECDBB <- mean((CD+BB)/2) > AVECDBC <- mean((CD+BC)/2) > AVECDBD <- mean((CD+BD)/2) > AVECDBE <- mean((CD+BE)/2) > AVECDBF <- mean((CD+BF)/2) > AVECDBG <- mean((CD+BG)/2) > AVECDBH <- mean((CD+BH)/2) > AVECDCA <- mean((CD+CA)/2) > AVECDCB <- mean((CD+CB)/2) > AVECDCC <- mean((CD+CC)/2) > AVECDCD <- mean((CD+CD)/2) > AVECDCE <- mean((CD+CE)/2) > AVECDCF <- mean((CD+CF)/2) > AVECDCG <- mean((CD+CG)/2) > AVECDCH <- mean((CD+CH)/2) > AVECEAA <- mean((CE+AA)/2) > AVECEAB <- mean((CE+AB)/2) > AVECEAC <- mean((CE+AC)/2) > AVECEAD <- mean((CE+AD)/2) > AVECEAE <- mean((CE+AE)/2) > AVECEAF <- mean((CE+AF)/2) > AVECEAG <- mean((CE+AG)/2) > AVECEAH <- mean((CE+AH)/2) > AVECEBA <- mean((CE+BA)/2) > AVECEBB <- mean((CE+BB)/2) > AVECEBC <- mean((CE+BC)/2) > AVECEBD <- mean((CE+BD)/2) > AVECEBE <- mean((CE+BE)/2) > AVECEBF <- mean((CE+BF)/2) > AVECEBG <- mean((CE+BG)/2) > AVECEBH <- mean((CE+BH)/2) > AVECECA <- mean((CE+CA)/2) > AVECECB <- mean((CE+CB)/2) > AVECECC <- mean((CE+CC)/2) > AVECECD <- mean((CE+CD)/2) > AVECECE <- mean((CE+CE)/2) > AVECECF <- mean((CE+CF)/2) > AVECECG <- mean((CE+CG)/2) > AVECECH <- mean((CE+CH)/2) > AVECFAA <- mean((CF+AA)/2) > AVECFAB <- mean((CF+AB)/2) > AVECFAC <- mean((CF+AC)/2) > AVECFAD <- mean((CF+AD)/2) > AVECFAE <- mean((CF+AE)/2) > AVECFAF <- mean((CF+AF)/2) > AVECFAG <- mean((CF+AG)/2) > AVECFAH <- mean((CF+AH)/2) > AVECFBA <- mean((CF+BA)/2) > AVECFBB <- mean((CF+BB)/2) > AVECFBC <- mean((CF+BC)/2) > AVECFBD <- mean((CF+BD)/2) > AVECFBE <- mean((CF+BE)/2) > AVECFBF <- mean((CF+BF)/2) > AVECFBG <- mean((CF+BG)/2) > AVECFBH <- mean((CF+BH)/2) > AVECFCA <- mean((CF+CA)/2) > AVECFCB <- mean((CF+CB)/2) > AVECFCC <- mean((CF+CC)/2) > AVECFCD <- mean((CF+CD)/2) > AVECFCE <- mean((CF+CE)/2) > AVECFCF <- mean((CF+CF)/2) > AVECFCG <- mean((CF+CG)/2) > AVECFCH <- mean((CF+CH)/2) > AVECGAA <- mean((CG+AA)/2) > AVECGAB <- mean((CG+AB)/2) > AVECGAC <- mean((CG+AC)/2) > AVECGAD <- mean((CG+AD)/2) > AVECGAE <- mean((CG+AE)/2) > AVECGAF <- mean((CG+AF)/2) > AVECGAG <- mean((CG+AG)/2) > AVECGAH <- mean((CG+AH)/2) > AVECGBA <- mean((CG+BA)/2) > AVECGBB <- mean((CG+BB)/2) > AVECGBC <- mean((CG+BC)/2) > AVECGBD <- mean((CG+BD)/2) > AVECGBE <- mean((CG+BE)/2) > AVECGBF <- mean((CG+BF)/2) > AVECGBG <- mean((CG+BG)/2) > AVECGBH <- mean((CG+BH)/2) > AVECGCA <- mean((CG+CA)/2) > AVECGCB <- mean((CG+CB)/2) > AVECGCC <- mean((CG+CC)/2) > AVECGCD <- mean((CG+CD)/2) > AVECGCE <- mean((CG+CE)/2) > AVECGCF <- mean((CG+CF)/2) > AVECGCG <- mean((CG+CG)/2) > AVECGCH <- mean((CG+CH)/2) > AVECHAA <- mean((CH+AA)/2) > AVECHAB <- mean((CH+AB)/2) > AVECHAC <- mean((CH+AC)/2) > AVECHAD <- mean((CH+AD)/2) > AVECHAE <- mean((CH+AE)/2) > AVECHAF <- mean((CH+AF)/2) > AVECHAG <- mean((CH+AG)/2) > AVECHAH <- mean((CH+AH)/2) > AVECHBA <- mean((CH+BA)/2) > AVECHBB <- mean((CH+BB)/2) > AVECHBC <- mean((CH+BC)/2) > AVECHBD <- mean((CH+BD)/2) > AVECHBE <- mean((CH+BE)/2) > AVECHBF <- mean((CH+BF)/2) > AVECHBG <- mean((CH+BG)/2) > AVECHBH <- mean((CH+BH)/2) > AVECHCA <- mean((CH+CA)/2) > AVECHCB <- mean((CH+CB)/2) > AVECHCC <- mean((CH+CC)/2) > AVECHCD <- mean((CH+CD)/2) > AVECHCE <- mean((CH+CE)/2) > AVECHCF <- mean((CH+CF)/2) > AVECHCG <- mean((CH+CG)/2) > AVECHCH <- mean((CH+CH)/2) > dAAAA <- numeric() > bAAAA <- 0 > cAAAA <- 0 > MNAAAA <- 0 > dAAAB <- numeric() > bAAAB <- 0 > cAAAB <- 0 > MNAAAB <- 0 > dAAAC <- numeric() > bAAAC <- 0 > cAAAC <- 0 > MNAAAC <- 0 > dAAAD <- numeric() > bAAAD <- 0 > cAAAD <- 0 > MNAAAD <- 0 > dAAAE <- numeric() > bAAAE <- 0 > cAAAE <- 0 > MNAAAE <- 0 > dAAAF <- numeric() > bAAAF <- 0 > cAAAF <- 0 > MNAAAF <- 0 > dAAAG <- numeric() > bAAAG <- 0 > cAAAG <- 0 > MNAAAG <- 0 > dAAAH <- numeric() > bAAAH <- 0 > cAAAH <- 0 > MNAAAH <- 0 > dAABA <- numeric() > bAABA <- 0 > cAABA <- 0 > MNAABA <- 0 > dAABB <- numeric() > bAABB <- 0 > cAABB <- 0 > MNAABB <- 0 > dAABC <- numeric() > bAABC <- 0 > cAABC <- 0 > MNAABC <- 0 > dAABD <- numeric() > bAABD <- 0 > cAABD <- 0 > MNAABD <- 0 > dAABE <- numeric() > bAABE <- 0 > cAABE <- 0 > MNAABE <- 0 > dAABF <- numeric() > bAABF <- 0 > cAABF <- 0 > MNAABF <- 0 > dAABG <- numeric() > bAABG <- 0 > cAABG <- 0 > MNAABG <- 0 > dAABH <- numeric() > bAABH <- 0 > cAABH <- 0 > MNAABH <- 0 > dAACA <- numeric() > bAACA <- 0 > cAACA <- 0 > MNAACA <- 0 > dAACB <- numeric() > bAACB <- 0 > cAACB <- 0 > MNAACB <- 0 > dAACC <- numeric() > bAACC <- 0 > cAACC <- 0 > MNAACC <- 0 > dAACD <- numeric() > bAACD <- 0 > cAACD <- 0 > MNAACD <- 0 > dAACE <- numeric() > bAACE <- 0 > cAACE <- 0 > MNAACE <- 0 > dAACF <- numeric() > bAACF <- 0 > cAACF <- 0 > MNAACF <- 0 > dAACG <- numeric() > bAACG <- 0 > cAACG <- 0 > MNAACG <- 0 > dAACH <- numeric() > bAACH <- 0 > cAACH <- 0 > MNAACH <- 0 > dABAA <- numeric() > bABAA <- 0 > cABAA <- 0 > MNABAA <- 0 > dABAB <- numeric() > bABAB <- 0 > cABAB <- 0 > MNABAB <- 0 > dABAC <- numeric() > bABAC <- 0 > cABAC <- 0 > MNABAC <- 0 > dABAD <- numeric() > bABAD <- 0 > cABAD <- 0 > MNABAD <- 0 > dABAE <- numeric() > bABAE <- 0 > cABAE <- 0 > MNABAE <- 0 > dABAF <- numeric() > bABAF <- 0 > cABAF <- 0 > MNABAF <- 0 > dABAG <- numeric() > bABAG <- 0 > cABAG <- 0 > MNABAG <- 0 > dABAH <- numeric() > bABAH <- 0 > cABAH <- 0 > MNABAH <- 0 > dABBA <- numeric() > bABBA <- 0 > cABBA <- 0 > MNABBA <- 0 > dABBB <- numeric() > bABBB <- 0 > cABBB <- 0 > MNABBB <- 0 > dABBC <- numeric() > bABBC <- 0 > cABBC <- 0 > MNABBC <- 0 > dABBD <- numeric() > bABBD <- 0 > cABBD <- 0 > MNABBD <- 0 > dABBE <- numeric() > bABBE <- 0 > cABBE <- 0 > MNABBE <- 0 > dABBF <- numeric() > bABBF <- 0 > cABBF <- 0 > MNABBF <- 0 > dABBG <- numeric() > bABBG <- 0 > cABBG <- 0 > MNABBG <- 0 > dABBH <- numeric() > bABBH <- 0 > cABBH <- 0 > MNABBH <- 0 > dABCA <- numeric() > bABCA <- 0 > cABCA <- 0 > MNABCA <- 0 > dABCB <- numeric() > bABCB <- 0 > cABCB <- 0 > MNABCB <- 0 > dABCC <- numeric() > bABCC <- 0 > cABCC <- 0 > MNABCC <- 0 > dABCD <- numeric() > bABCD <- 0 > cABCD <- 0 > MNABCD <- 0 > dABCE <- numeric() > bABCE <- 0 > cABCE <- 0 > MNABCE <- 0 > dABCF <- numeric() > bABCF <- 0 > cABCF <- 0 > MNABCF <- 0 > dABCG <- numeric() > bABCG <- 0 > cABCG <- 0 > MNABCG <- 0 > dABCH <- numeric() > bABCH <- 0 > cABCH <- 0 > MNABCH <- 0 > dACAA <- numeric() > bACAA <- 0 > cACAA <- 0 > MNACAA <- 0 > dACAB <- numeric() > bACAB <- 0 > cACAB <- 0 > MNACAB <- 0 > dACAC <- numeric() > bACAC <- 0 > cACAC <- 0 > MNACAC <- 0 > dACAD <- numeric() > bACAD <- 0 > cACAD <- 0 > MNACAD <- 0 > dACAE <- numeric() > bACAE <- 0 > cACAE <- 0 > MNACAE <- 0 > dACAF <- numeric() > bACAF <- 0 > cACAF <- 0 > MNACAF <- 0 > dACAG <- numeric() > bACAG <- 0 > cACAG <- 0 > MNACAG <- 0 > dACAH <- numeric() > bACAH <- 0 > cACAH <- 0 > MNACAH <- 0 > dACBA <- numeric() > bACBA <- 0 > cACBA <- 0 > MNACBA <- 0 > dACBB <- numeric() > bACBB <- 0 > cACBB <- 0 > MNACBB <- 0 > dACBC <- numeric() > bACBC <- 0 > cACBC <- 0 > MNACBC <- 0 > dACBD <- numeric() > bACBD <- 0 > cACBD <- 0 > MNACBD <- 0 > dACBE <- numeric() > bACBE <- 0 > cACBE <- 0 > MNACBE <- 0 > dACBF <- numeric() > bACBF <- 0 > cACBF <- 0 > MNACBF <- 0 > dACBG <- numeric() > bACBG <- 0 > cACBG <- 0 > MNACBG <- 0 > dACBH <- numeric() > bACBH <- 0 > cACBH <- 0 > MNACBH <- 0 > dACCA <- numeric() > bACCA <- 0 > cACCA <- 0 > MNACCA <- 0 > dACCB <- numeric() > bACCB <- 0 > cACCB <- 0 > MNACCB <- 0 > dACCC <- numeric() > bACCC <- 0 > cACCC <- 0 > MNACCC <- 0 > dACCD <- numeric() > bACCD <- 0 > cACCD <- 0 > MNACCD <- 0 > dACCE <- numeric() > bACCE <- 0 > cACCE <- 0 > MNACCE <- 0 > dACCF <- numeric() > bACCF <- 0 > cACCF <- 0 > MNACCF <- 0 > dACCG <- numeric() > bACCG <- 0 > cACCG <- 0 > MNACCG <- 0 > dACCH <- numeric() > bACCH <- 0 > cACCH <- 0 > MNACCH <- 0 > dADAA <- numeric() > bADAA <- 0 > cADAA <- 0 > MNADAA <- 0 > dADAB <- numeric() > bADAB <- 0 > cADAB <- 0 > MNADAB <- 0 > dADAC <- numeric() > bADAC <- 0 > cADAC <- 0 > MNADAC <- 0 > dADAD <- numeric() > bADAD <- 0 > cADAD <- 0 > MNADAD <- 0 > dADAE <- numeric() > bADAE <- 0 > cADAE <- 0 > MNADAE <- 0 > dADAF <- numeric() > bADAF <- 0 > cADAF <- 0 > MNADAF <- 0 > dADAG <- numeric() > bADAG <- 0 > cADAG <- 0 > MNADAG <- 0 > dADAH <- numeric() > bADAH <- 0 > cADAH <- 0 > MNADAH <- 0 > dADBA <- numeric() > bADBA <- 0 > cADBA <- 0 > MNADBA <- 0 > dADBB <- numeric() > bADBB <- 0 > cADBB <- 0 > MNADBB <- 0 > dADBC <- numeric() > bADBC <- 0 > cADBC <- 0 > MNADBC <- 0 > dADBD <- numeric() > bADBD <- 0 > cADBD <- 0 > MNADBD <- 0 > dADBE <- numeric() > bADBE <- 0 > cADBE <- 0 > MNADBE <- 0 > dADBF <- numeric() > bADBF <- 0 > cADBF <- 0 > MNADBF <- 0 > dADBG <- numeric() > bADBG <- 0 > cADBG <- 0 > MNADBG <- 0 > dADBH <- numeric() > bADBH <- 0 > cADBH <- 0 > MNADBH <- 0 > dADCA <- numeric() > bADCA <- 0 > cADCA <- 0 > MNADCA <- 0 > dADCB <- numeric() > bADCB <- 0 > cADCB <- 0 > MNADCB <- 0 > dADCC <- numeric() > bADCC <- 0 > cADCC <- 0 > MNADCC <- 0 > dADCD <- numeric() > bADCD <- 0 > cADCD <- 0 > MNADCD <- 0 > dADCE <- numeric() > bADCE <- 0 > cADCE <- 0 > MNADCE <- 0 > dADCF <- numeric() > bADCF <- 0 > cADCF <- 0 > MNADCF <- 0 > dADCG <- numeric() > bADCG <- 0 > cADCG <- 0 > MNADCG <- 0 > dADCH <- numeric() > bADCH <- 0 > cADCH <- 0 > MNADCH <- 0 > dAEAA <- numeric() > bAEAA <- 0 > cAEAA <- 0 > MNAEAA <- 0 > dAEAB <- numeric() > bAEAB <- 0 > cAEAB <- 0 > MNAEAB <- 0 > dAEAC <- numeric() > bAEAC <- 0 > cAEAC <- 0 > MNAEAC <- 0 > dAEAD <- numeric() > bAEAD <- 0 > cAEAD <- 0 > MNAEAD <- 0 > dAEAE <- numeric() > bAEAE <- 0 > cAEAE <- 0 > MNAEAE <- 0 > dAEAF <- numeric() > bAEAF <- 0 > cAEAF <- 0 > MNAEAF <- 0 > dAEAG <- numeric() > bAEAG <- 0 > cAEAG <- 0 > MNAEAG <- 0 > dAEAH <- numeric() > bAEAH <- 0 > cAEAH <- 0 > MNAEAH <- 0 > dAEBA <- numeric() > bAEBA <- 0 > cAEBA <- 0 > MNAEBA <- 0 > dAEBB <- numeric() > bAEBB <- 0 > cAEBB <- 0 > MNAEBB <- 0 > dAEBC <- numeric() > bAEBC <- 0 > cAEBC <- 0 > MNAEBC <- 0 > dAEBD <- numeric() > bAEBD <- 0 > cAEBD <- 0 > MNAEBD <- 0 > dAEBE <- numeric() > bAEBE <- 0 > cAEBE <- 0 > MNAEBE <- 0 > dAEBF <- numeric() > bAEBF <- 0 > cAEBF <- 0 > MNAEBF <- 0 > dAEBG <- numeric() > bAEBG <- 0 > cAEBG <- 0 > MNAEBG <- 0 > dAEBH <- numeric() > bAEBH <- 0 > cAEBH <- 0 > MNAEBH <- 0 > dAECA <- numeric() > bAECA <- 0 > cAECA <- 0 > MNAECA <- 0 > dAECB <- numeric() > bAECB <- 0 > cAECB <- 0 > MNAECB <- 0 > dAECC <- numeric() > bAECC <- 0 > cAECC <- 0 > MNAECC <- 0 > dAECD <- numeric() > bAECD <- 0 > cAECD <- 0 > MNAECD <- 0 > dAECE <- numeric() > bAECE <- 0 > cAECE <- 0 > MNAECE <- 0 > dAECF <- numeric() > bAECF <- 0 > cAECF <- 0 > MNAECF <- 0 > dAECG <- numeric() > bAECG <- 0 > cAECG <- 0 > MNAECG <- 0 > dAECH <- numeric() > bAECH <- 0 > cAECH <- 0 > MNAECH <- 0 > dAFAA <- numeric() > bAFAA <- 0 > cAFAA <- 0 > MNAFAA <- 0 > dAFAB <- numeric() > bAFAB <- 0 > cAFAB <- 0 > MNAFAB <- 0 > dAFAC <- numeric() > bAFAC <- 0 > cAFAC <- 0 > MNAFAC <- 0 > dAFAD <- numeric() > bAFAD <- 0 > cAFAD <- 0 > MNAFAD <- 0 > dAFAE <- numeric() > bAFAE <- 0 > cAFAE <- 0 > MNAFAE <- 0 > dAFAF <- numeric() > bAFAF <- 0 > cAFAF <- 0 > MNAFAF <- 0 > dAFAG <- numeric() > bAFAG <- 0 > cAFAG <- 0 > MNAFAG <- 0 > dAFAH <- numeric() > bAFAH <- 0 > cAFAH <- 0 > MNAFAH <- 0 > dAFBA <- numeric() > bAFBA <- 0 > cAFBA <- 0 > MNAFBA <- 0 > dAFBB <- numeric() > bAFBB <- 0 > cAFBB <- 0 > MNAFBB <- 0 > dAFBC <- numeric() > bAFBC <- 0 > cAFBC <- 0 > MNAFBC <- 0 > dAFBD <- numeric() > bAFBD <- 0 > cAFBD <- 0 > MNAFBD <- 0 > dAFBE <- numeric() > bAFBE <- 0 > cAFBE <- 0 > MNAFBE <- 0 > dAFBF <- numeric() > bAFBF <- 0 > cAFBF <- 0 > MNAFBF <- 0 > dAFBG <- numeric() > bAFBG <- 0 > cAFBG <- 0 > MNAFBG <- 0 > dAFBH <- numeric() > bAFBH <- 0 > cAFBH <- 0 > MNAFBH <- 0 > dAFCA <- numeric() > bAFCA <- 0 > cAFCA <- 0 > MNAFCA <- 0 > dAFCB <- numeric() > bAFCB <- 0 > cAFCB <- 0 > MNAFCB <- 0 > dAFCC <- numeric() > bAFCC <- 0 > cAFCC <- 0 > MNAFCC <- 0 > dAFCD <- numeric() > bAFCD <- 0 > cAFCD <- 0 > MNAFCD <- 0 > dAFCE <- numeric() > bAFCE <- 0 > cAFCE <- 0 > MNAFCE <- 0 > dAFCF <- numeric() > bAFCF <- 0 > cAFCF <- 0 > MNAFCF <- 0 > dAFCG <- numeric() > bAFCG <- 0 > cAFCG <- 0 > MNAFCG <- 0 > dAFCH <- numeric() > bAFCH <- 0 > cAFCH <- 0 > MNAFCH <- 0 > dAGAA <- numeric() > bAGAA <- 0 > cAGAA <- 0 > MNAGAA <- 0 > dAGAB <- numeric() > bAGAB <- 0 > cAGAB <- 0 > MNAGAB <- 0 > dAGAC <- numeric() > bAGAC <- 0 > cAGAC <- 0 > MNAGAC <- 0 > dAGAD <- numeric() > bAGAD <- 0 > cAGAD <- 0 > MNAGAD <- 0 > dAGAE <- numeric() > bAGAE <- 0 > cAGAE <- 0 > MNAGAE <- 0 > dAGAF <- numeric() > bAGAF <- 0 > cAGAF <- 0 > MNAGAF <- 0 > dAGAG <- numeric() > bAGAG <- 0 > cAGAG <- 0 > MNAGAG <- 0 > dAGAH <- numeric() > bAGAH <- 0 > cAGAH <- 0 > MNAGAH <- 0 > dAGBA <- numeric() > bAGBA <- 0 > cAGBA <- 0 > MNAGBA <- 0 > dAGBB <- numeric() > bAGBB <- 0 > cAGBB <- 0 > MNAGBB <- 0 > dAGBC <- numeric() > bAGBC <- 0 > cAGBC <- 0 > MNAGBC <- 0 > dAGBD <- numeric() > bAGBD <- 0 > cAGBD <- 0 > MNAGBD <- 0 > dAGBE <- numeric() > bAGBE <- 0 > cAGBE <- 0 > MNAGBE <- 0 > dAGBF <- numeric() > bAGBF <- 0 > cAGBF <- 0 > MNAGBF <- 0 > dAGBG <- numeric() > bAGBG <- 0 > cAGBG <- 0 > MNAGBG <- 0 > dAGBH <- numeric() > bAGBH <- 0 > cAGBH <- 0 > MNAGBH <- 0 > dAGCA <- numeric() > bAGCA <- 0 > cAGCA <- 0 > MNAGCA <- 0 > dAGCB <- numeric() > bAGCB <- 0 > cAGCB <- 0 > MNAGCB <- 0 > dAGCC <- numeric() > bAGCC <- 0 > cAGCC <- 0 > MNAGCC <- 0 > dAGCD <- numeric() > bAGCD <- 0 > cAGCD <- 0 > MNAGCD <- 0 > dAGCE <- numeric() > bAGCE <- 0 > cAGCE <- 0 > MNAGCE <- 0 > dAGCF <- numeric() > bAGCF <- 0 > cAGCF <- 0 > MNAGCF <- 0 > dAGCG <- numeric() > bAGCG <- 0 > cAGCG <- 0 > MNAGCG <- 0 > dAGCH <- numeric() > bAGCH <- 0 > cAGCH <- 0 > MNAGCH <- 0 > dAHAA <- numeric() > bAHAA <- 0 > cAHAA <- 0 > MNAHAA <- 0 > dAHAB <- numeric() > bAHAB <- 0 > cAHAB <- 0 > MNAHAB <- 0 > dAHAC <- numeric() > bAHAC <- 0 > cAHAC <- 0 > MNAHAC <- 0 > dAHAD <- numeric() > bAHAD <- 0 > cAHAD <- 0 > MNAHAD <- 0 > dAHAE <- numeric() > bAHAE <- 0 > cAHAE <- 0 > MNAHAE <- 0 > dAHAF <- numeric() > bAHAF <- 0 > cAHAF <- 0 > MNAHAF <- 0 > dAHAG <- numeric() > bAHAG <- 0 > cAHAG <- 0 > MNAHAG <- 0 > dAHAH <- numeric() > bAHAH <- 0 > cAHAH <- 0 > MNAHAH <- 0 > dAHBA <- numeric() > bAHBA <- 0 > cAHBA <- 0 > MNAHBA <- 0 > dAHBB <- numeric() > bAHBB <- 0 > cAHBB <- 0 > MNAHBB <- 0 > dAHBC <- numeric() > bAHBC <- 0 > cAHBC <- 0 > MNAHBC <- 0 > dAHBD <- numeric() > bAHBD <- 0 > cAHBD <- 0 > MNAHBD <- 0 > dAHBE <- numeric() > bAHBE <- 0 > cAHBE <- 0 > MNAHBE <- 0 > dAHBF <- numeric() > bAHBF <- 0 > cAHBF <- 0 > MNAHBF <- 0 > dAHBG <- numeric() > bAHBG <- 0 > cAHBG <- 0 > MNAHBG <- 0 > dAHBH <- numeric() > bAHBH <- 0 > cAHBH <- 0 > MNAHBH <- 0 > dAHCA <- numeric() > bAHCA <- 0 > cAHCA <- 0 > MNAHCA <- 0 > dAHCB <- numeric() > bAHCB <- 0 > cAHCB <- 0 > MNAHCB <- 0 > dAHCC <- numeric() > bAHCC <- 0 > cAHCC <- 0 > MNAHCC <- 0 > dAHCD <- numeric() > bAHCD <- 0 > cAHCD <- 0 > MNAHCD <- 0 > dAHCE <- numeric() > bAHCE <- 0 > cAHCE <- 0 > MNAHCE <- 0 > dAHCF <- numeric() > bAHCF <- 0 > cAHCF <- 0 > MNAHCF <- 0 > dAHCG <- numeric() > bAHCG <- 0 > cAHCG <- 0 > MNAHCG <- 0 > dAHCH <- numeric() > bAHCH <- 0 > cAHCH <- 0 > MNAHCH <- 0 > dBAAA <- numeric() > bBAAA <- 0 > cBAAA <- 0 > MNBAAA <- 0 > dBAAB <- numeric() > bBAAB <- 0 > cBAAB <- 0 > MNBAAB <- 0 > dBAAC <- numeric() > bBAAC <- 0 > cBAAC <- 0 > MNBAAC <- 0 > dBAAD <- numeric() > bBAAD <- 0 > cBAAD <- 0 > MNBAAD <- 0 > dBAAE <- numeric() > bBAAE <- 0 > cBAAE <- 0 > MNBAAE <- 0 > dBAAF <- numeric() > bBAAF <- 0 > cBAAF <- 0 > MNBAAF <- 0 > dBAAG <- numeric() > bBAAG <- 0 > cBAAG <- 0 > MNBAAG <- 0 > dBAAH <- numeric() > bBAAH <- 0 > cBAAH <- 0 > MNBAAH <- 0 > dBABA <- numeric() > bBABA <- 0 > cBABA <- 0 > MNBABA <- 0 > dBABB <- numeric() > bBABB <- 0 > cBABB <- 0 > MNBABB <- 0 > dBABC <- numeric() > bBABC <- 0 > cBABC <- 0 > MNBABC <- 0 > dBABD <- numeric() > bBABD <- 0 > cBABD <- 0 > MNBABD <- 0 > dBABE <- numeric() > bBABE <- 0 > cBABE <- 0 > MNBABE <- 0 > dBABF <- numeric() > bBABF <- 0 > cBABF <- 0 > MNBABF <- 0 > dBABG <- numeric() > bBABG <- 0 > cBABG <- 0 > MNBABG <- 0 > dBABH <- numeric() > bBABH <- 0 > cBABH <- 0 > MNBABH <- 0 > dBACA <- numeric() > bBACA <- 0 > cBACA <- 0 > MNBACA <- 0 > dBACB <- numeric() > bBACB <- 0 > cBACB <- 0 > MNBACB <- 0 > dBACC <- numeric() > bBACC <- 0 > cBACC <- 0 > MNBACC <- 0 > dBACD <- numeric() > bBACD <- 0 > cBACD <- 0 > MNBACD <- 0 > dBACE <- numeric() > bBACE <- 0 > cBACE <- 0 > MNBACE <- 0 > dBACF <- numeric() > bBACF <- 0 > cBACF <- 0 > MNBACF <- 0 > dBACG <- numeric() > bBACG <- 0 > cBACG <- 0 > MNBACG <- 0 > dBACH <- numeric() > bBACH <- 0 > cBACH <- 0 > MNBACH <- 0 > dBBAA <- numeric() > bBBAA <- 0 > cBBAA <- 0 > MNBBAA <- 0 > dBBAB <- numeric() > bBBAB <- 0 > cBBAB <- 0 > MNBBAB <- 0 > dBBAC <- numeric() > bBBAC <- 0 > cBBAC <- 0 > MNBBAC <- 0 > dBBAD <- numeric() > bBBAD <- 0 > cBBAD <- 0 > MNBBAD <- 0 > dBBAE <- numeric() > bBBAE <- 0 > cBBAE <- 0 > MNBBAE <- 0 > dBBAF <- numeric() > bBBAF <- 0 > cBBAF <- 0 > MNBBAF <- 0 > dBBAG <- numeric() > bBBAG <- 0 > cBBAG <- 0 > MNBBAG <- 0 > dBBAH <- numeric() > bBBAH <- 0 > cBBAH <- 0 > MNBBAH <- 0 > dBBBA <- numeric() > bBBBA <- 0 > cBBBA <- 0 > MNBBBA <- 0 > dBBBB <- numeric() > bBBBB <- 0 > cBBBB <- 0 > MNBBBB <- 0 > dBBBC <- numeric() > bBBBC <- 0 > cBBBC <- 0 > MNBBBC <- 0 > dBBBD <- numeric() > bBBBD <- 0 > cBBBD <- 0 > MNBBBD <- 0 > dBBBE <- numeric() > bBBBE <- 0 > cBBBE <- 0 > MNBBBE <- 0 > dBBBF <- numeric() > bBBBF <- 0 > cBBBF <- 0 > MNBBBF <- 0 > dBBBG <- numeric() > bBBBG <- 0 > cBBBG <- 0 > MNBBBG <- 0 > dBBBH <- numeric() > bBBBH <- 0 > cBBBH <- 0 > MNBBBH <- 0 > dBBCA <- numeric() > bBBCA <- 0 > cBBCA <- 0 > MNBBCA <- 0 > dBBCB <- numeric() > bBBCB <- 0 > cBBCB <- 0 > MNBBCB <- 0 > dBBCC <- numeric() > bBBCC <- 0 > cBBCC <- 0 > MNBBCC <- 0 > dBBCD <- numeric() > bBBCD <- 0 > cBBCD <- 0 > MNBBCD <- 0 > dBBCE <- numeric() > bBBCE <- 0 > cBBCE <- 0 > MNBBCE <- 0 > dBBCF <- numeric() > bBBCF <- 0 > cBBCF <- 0 > MNBBCF <- 0 > dBBCG <- numeric() > bBBCG <- 0 > cBBCG <- 0 > MNBBCG <- 0 > dBBCH <- numeric() > bBBCH <- 0 > cBBCH <- 0 > MNBBCH <- 0 > dBCAA <- numeric() > bBCAA <- 0 > cBCAA <- 0 > MNBCAA <- 0 > dBCAB <- numeric() > bBCAB <- 0 > cBCAB <- 0 > MNBCAB <- 0 > dBCAC <- numeric() > bBCAC <- 0 > cBCAC <- 0 > MNBCAC <- 0 > dBCAD <- numeric() > bBCAD <- 0 > cBCAD <- 0 > MNBCAD <- 0 > dBCAE <- numeric() > bBCAE <- 0 > cBCAE <- 0 > MNBCAE <- 0 > dBCAF <- numeric() > bBCAF <- 0 > cBCAF <- 0 > MNBCAF <- 0 > dBCAG <- numeric() > bBCAG <- 0 > cBCAG <- 0 > MNBCAG <- 0 > dBCAH <- numeric() > bBCAH <- 0 > cBCAH <- 0 > MNBCAH <- 0 > dBCBA <- numeric() > bBCBA <- 0 > cBCBA <- 0 > MNBCBA <- 0 > dBCBB <- numeric() > bBCBB <- 0 > cBCBB <- 0 > MNBCBB <- 0 > dBCBC <- numeric() > bBCBC <- 0 > cBCBC <- 0 > MNBCBC <- 0 > dBCBD <- numeric() > bBCBD <- 0 > cBCBD <- 0 > MNBCBD <- 0 > dBCBE <- numeric() > bBCBE <- 0 > cBCBE <- 0 > MNBCBE <- 0 > dBCBF <- numeric() > bBCBF <- 0 > cBCBF <- 0 > MNBCBF <- 0 > dBCBG <- numeric() > bBCBG <- 0 > cBCBG <- 0 > MNBCBG <- 0 > dBCBH <- numeric() > bBCBH <- 0 > cBCBH <- 0 > MNBCBH <- 0 > dBCCA <- numeric() > bBCCA <- 0 > cBCCA <- 0 > MNBCCA <- 0 > dBCCB <- numeric() > bBCCB <- 0 > cBCCB <- 0 > MNBCCB <- 0 > dBCCC <- numeric() > bBCCC <- 0 > cBCCC <- 0 > MNBCCC <- 0 > dBCCD <- numeric() > bBCCD <- 0 > cBCCD <- 0 > MNBCCD <- 0 > dBCCE <- numeric() > bBCCE <- 0 > cBCCE <- 0 > MNBCCE <- 0 > dBCCF <- numeric() > bBCCF <- 0 > cBCCF <- 0 > MNBCCF <- 0 > dBCCG <- numeric() > bBCCG <- 0 > cBCCG <- 0 > MNBCCG <- 0 > dBCCH <- numeric() > bBCCH <- 0 > cBCCH <- 0 > MNBCCH <- 0 > dBDAA <- numeric() > bBDAA <- 0 > cBDAA <- 0 > MNBDAA <- 0 > dBDAB <- numeric() > bBDAB <- 0 > cBDAB <- 0 > MNBDAB <- 0 > dBDAC <- numeric() > bBDAC <- 0 > cBDAC <- 0 > MNBDAC <- 0 > dBDAD <- numeric() > bBDAD <- 0 > cBDAD <- 0 > MNBDAD <- 0 > dBDAE <- numeric() > bBDAE <- 0 > cBDAE <- 0 > MNBDAE <- 0 > dBDAF <- numeric() > bBDAF <- 0 > cBDAF <- 0 > MNBDAF <- 0 > dBDAG <- numeric() > bBDAG <- 0 > cBDAG <- 0 > MNBDAG <- 0 > dBDAH <- numeric() > bBDAH <- 0 > cBDAH <- 0 > MNBDAH <- 0 > dBDBA <- numeric() > bBDBA <- 0 > cBDBA <- 0 > MNBDBA <- 0 > dBDBB <- numeric() > bBDBB <- 0 > cBDBB <- 0 > MNBDBB <- 0 > dBDBC <- numeric() > bBDBC <- 0 > cBDBC <- 0 > MNBDBC <- 0 > dBDBD <- numeric() > bBDBD <- 0 > cBDBD <- 0 > MNBDBD <- 0 > dBDBE <- numeric() > bBDBE <- 0 > cBDBE <- 0 > MNBDBE <- 0 > dBDBF <- numeric() > bBDBF <- 0 > cBDBF <- 0 > MNBDBF <- 0 > dBDBG <- numeric() > bBDBG <- 0 > cBDBG <- 0 > MNBDBG <- 0 > dBDBH <- numeric() > bBDBH <- 0 > cBDBH <- 0 > MNBDBH <- 0 > dBDCA <- numeric() > bBDCA <- 0 > cBDCA <- 0 > MNBDCA <- 0 > dBDCB <- numeric() > bBDCB <- 0 > cBDCB <- 0 > MNBDCB <- 0 > dBDCC <- numeric() > bBDCC <- 0 > cBDCC <- 0 > MNBDCC <- 0 > dBDCD <- numeric() > bBDCD <- 0 > cBDCD <- 0 > MNBDCD <- 0 > dBDCE <- numeric() > bBDCE <- 0 > cBDCE <- 0 > MNBDCE <- 0 > dBDCF <- numeric() > bBDCF <- 0 > cBDCF <- 0 > MNBDCF <- 0 > dBDCG <- numeric() > bBDCG <- 0 > cBDCG <- 0 > MNBDCG <- 0 > dBDCH <- numeric() > bBDCH <- 0 > cBDCH <- 0 > MNBDCH <- 0 > dBEAA <- numeric() > bBEAA <- 0 > cBEAA <- 0 > MNBEAA <- 0 > dBEAB <- numeric() > bBEAB <- 0 > cBEAB <- 0 > MNBEAB <- 0 > dBEAC <- numeric() > bBEAC <- 0 > cBEAC <- 0 > MNBEAC <- 0 > dBEAD <- numeric() > bBEAD <- 0 > cBEAD <- 0 > MNBEAD <- 0 > dBEAE <- numeric() > bBEAE <- 0 > cBEAE <- 0 > MNBEAE <- 0 > dBEAF <- numeric() > bBEAF <- 0 > cBEAF <- 0 > MNBEAF <- 0 > dBEAG <- numeric() > bBEAG <- 0 > cBEAG <- 0 > MNBEAG <- 0 > dBEAH <- numeric() > bBEAH <- 0 > cBEAH <- 0 > MNBEAH <- 0 > dBEBA <- numeric() > bBEBA <- 0 > cBEBA <- 0 > MNBEBA <- 0 > dBEBB <- numeric() > bBEBB <- 0 > cBEBB <- 0 > MNBEBB <- 0 > dBEBC <- numeric() > bBEBC <- 0 > cBEBC <- 0 > MNBEBC <- 0 > dBEBD <- numeric() > bBEBD <- 0 > cBEBD <- 0 > MNBEBD <- 0 > dBEBE <- numeric() > bBEBE <- 0 > cBEBE <- 0 > MNBEBE <- 0 > dBEBF <- numeric() > bBEBF <- 0 > cBEBF <- 0 > MNBEBF <- 0 > dBEBG <- numeric() > bBEBG <- 0 > cBEBG <- 0 > MNBEBG <- 0 > dBEBH <- numeric() > bBEBH <- 0 > cBEBH <- 0 > MNBEBH <- 0 > dBECA <- numeric() > bBECA <- 0 > cBECA <- 0 > MNBECA <- 0 > dBECB <- numeric() > bBECB <- 0 > cBECB <- 0 > MNBECB <- 0 > dBECC <- numeric() > bBECC <- 0 > cBECC <- 0 > MNBECC <- 0 > dBECD <- numeric() > bBECD <- 0 > cBECD <- 0 > MNBECD <- 0 > dBECE <- numeric() > bBECE <- 0 > cBECE <- 0 > MNBECE <- 0 > dBECF <- numeric() > bBECF <- 0 > cBECF <- 0 > MNBECF <- 0 > dBECG <- numeric() > bBECG <- 0 > cBECG <- 0 > MNBECG <- 0 > dBECH <- numeric() > bBECH <- 0 > cBECH <- 0 > MNBECH <- 0 > dBFAA <- numeric() > bBFAA <- 0 > cBFAA <- 0 > MNBFAA <- 0 > dBFAB <- numeric() > bBFAB <- 0 > cBFAB <- 0 > MNBFAB <- 0 > dBFAC <- numeric() > bBFAC <- 0 > cBFAC <- 0 > MNBFAC <- 0 > dBFAD <- numeric() > bBFAD <- 0 > cBFAD <- 0 > MNBFAD <- 0 > dBFAE <- numeric() > bBFAE <- 0 > cBFAE <- 0 > MNBFAE <- 0 > dBFAF <- numeric() > bBFAF <- 0 > cBFAF <- 0 > MNBFAF <- 0 > dBFAG <- numeric() > bBFAG <- 0 > cBFAG <- 0 > MNBFAG <- 0 > dBFAH <- numeric() > bBFAH <- 0 > cBFAH <- 0 > MNBFAH <- 0 > dBFBA <- numeric() > bBFBA <- 0 > cBFBA <- 0 > MNBFBA <- 0 > dBFBB <- numeric() > bBFBB <- 0 > cBFBB <- 0 > MNBFBB <- 0 > dBFBC <- numeric() > bBFBC <- 0 > cBFBC <- 0 > MNBFBC <- 0 > dBFBD <- numeric() > bBFBD <- 0 > cBFBD <- 0 > MNBFBD <- 0 > dBFBE <- numeric() > bBFBE <- 0 > cBFBE <- 0 > MNBFBE <- 0 > dBFBF <- numeric() > bBFBF <- 0 > cBFBF <- 0 > MNBFBF <- 0 > dBFBG <- numeric() > bBFBG <- 0 > cBFBG <- 0 > MNBFBG <- 0 > dBFBH <- numeric() > bBFBH <- 0 > cBFBH <- 0 > MNBFBH <- 0 > dBFCA <- numeric() > bBFCA <- 0 > cBFCA <- 0 > MNBFCA <- 0 > dBFCB <- numeric() > bBFCB <- 0 > cBFCB <- 0 > MNBFCB <- 0 > dBFCC <- numeric() > bBFCC <- 0 > cBFCC <- 0 > MNBFCC <- 0 > dBFCD <- numeric() > bBFCD <- 0 > cBFCD <- 0 > MNBFCD <- 0 > dBFCE <- numeric() > bBFCE <- 0 > cBFCE <- 0 > MNBFCE <- 0 > dBFCF <- numeric() > bBFCF <- 0 > cBFCF <- 0 > MNBFCF <- 0 > dBFCG <- numeric() > bBFCG <- 0 > cBFCG <- 0 > MNBFCG <- 0 > dBFCH <- numeric() > bBFCH <- 0 > cBFCH <- 0 > MNBFCH <- 0 > dBGAA <- numeric() > bBGAA <- 0 > cBGAA <- 0 > MNBGAA <- 0 > dBGAB <- numeric() > bBGAB <- 0 > cBGAB <- 0 > MNBGAB <- 0 > dBGAC <- numeric() > bBGAC <- 0 > cBGAC <- 0 > MNBGAC <- 0 > dBGAD <- numeric() > bBGAD <- 0 > cBGAD <- 0 > MNBGAD <- 0 > dBGAE <- numeric() > bBGAE <- 0 > cBGAE <- 0 > MNBGAE <- 0 > dBGAF <- numeric() > bBGAF <- 0 > cBGAF <- 0 > MNBGAF <- 0 > dBGAG <- numeric() > bBGAG <- 0 > cBGAG <- 0 > MNBGAG <- 0 > dBGAH <- numeric() > bBGAH <- 0 > cBGAH <- 0 > MNBGAH <- 0 > dBGBA <- numeric() > bBGBA <- 0 > cBGBA <- 0 > MNBGBA <- 0 > dBGBB <- numeric() > bBGBB <- 0 > cBGBB <- 0 > MNBGBB <- 0 > dBGBC <- numeric() > bBGBC <- 0 > cBGBC <- 0 > MNBGBC <- 0 > dBGBD <- numeric() > bBGBD <- 0 > cBGBD <- 0 > MNBGBD <- 0 > dBGBE <- numeric() > bBGBE <- 0 > cBGBE <- 0 > MNBGBE <- 0 > dBGBF <- numeric() > bBGBF <- 0 > cBGBF <- 0 > MNBGBF <- 0 > dBGBG <- numeric() > bBGBG <- 0 > cBGBG <- 0 > MNBGBG <- 0 > dBGBH <- numeric() > bBGBH <- 0 > cBGBH <- 0 > MNBGBH <- 0 > dBGCA <- numeric() > bBGCA <- 0 > cBGCA <- 0 > MNBGCA <- 0 > dBGCB <- numeric() > bBGCB <- 0 > cBGCB <- 0 > MNBGCB <- 0 > dBGCC <- numeric() > bBGCC <- 0 > cBGCC <- 0 > MNBGCC <- 0 > dBGCD <- numeric() > bBGCD <- 0 > cBGCD <- 0 > MNBGCD <- 0 > dBGCE <- numeric() > bBGCE <- 0 > cBGCE <- 0 > MNBGCE <- 0 > dBGCF <- numeric() > bBGCF <- 0 > cBGCF <- 0 > MNBGCF <- 0 > dBGCG <- numeric() > bBGCG <- 0 > cBGCG <- 0 > MNBGCG <- 0 > dBGCH <- numeric() > bBGCH <- 0 > cBGCH <- 0 > MNBGCH <- 0 > dBHAA <- numeric() > bBHAA <- 0 > cBHAA <- 0 > MNBHAA <- 0 > dBHAB <- numeric() > bBHAB <- 0 > cBHAB <- 0 > MNBHAB <- 0 > dBHAC <- numeric() > bBHAC <- 0 > cBHAC <- 0 > MNBHAC <- 0 > dBHAD <- numeric() > bBHAD <- 0 > cBHAD <- 0 > MNBHAD <- 0 > dBHAE <- numeric() > bBHAE <- 0 > cBHAE <- 0 > MNBHAE <- 0 > dBHAF <- numeric() > bBHAF <- 0 > cBHAF <- 0 > MNBHAF <- 0 > dBHAG <- numeric() > bBHAG <- 0 > cBHAG <- 0 > MNBHAG <- 0 > dBHAH <- numeric() > bBHAH <- 0 > cBHAH <- 0 > MNBHAH <- 0 > dBHBA <- numeric() > bBHBA <- 0 > cBHBA <- 0 > MNBHBA <- 0 > dBHBB <- numeric() > bBHBB <- 0 > cBHBB <- 0 > MNBHBB <- 0 > dBHBC <- numeric() > bBHBC <- 0 > cBHBC <- 0 > MNBHBC <- 0 > dBHBD <- numeric() > bBHBD <- 0 > cBHBD <- 0 > MNBHBD <- 0 > dBHBE <- numeric() > bBHBE <- 0 > cBHBE <- 0 > MNBHBE <- 0 > dBHBF <- numeric() > bBHBF <- 0 > cBHBF <- 0 > MNBHBF <- 0 > dBHBG <- numeric() > bBHBG <- 0 > cBHBG <- 0 > MNBHBG <- 0 > dBHBH <- numeric() > bBHBH <- 0 > cBHBH <- 0 > MNBHBH <- 0 > dBHCA <- numeric() > bBHCA <- 0 > cBHCA <- 0 > MNBHCA <- 0 > dBHCB <- numeric() > bBHCB <- 0 > cBHCB <- 0 > MNBHCB <- 0 > dBHCC <- numeric() > bBHCC <- 0 > cBHCC <- 0 > MNBHCC <- 0 > dBHCD <- numeric() > bBHCD <- 0 > cBHCD <- 0 > MNBHCD <- 0 > dBHCE <- numeric() > bBHCE <- 0 > cBHCE <- 0 > MNBHCE <- 0 > dBHCF <- numeric() > bBHCF <- 0 > cBHCF <- 0 > MNBHCF <- 0 > dBHCG <- numeric() > bBHCG <- 0 > cBHCG <- 0 > MNBHCG <- 0 > dBHCH <- numeric() > bBHCH <- 0 > cBHCH <- 0 > MNBHCH <- 0 > dCAAA <- numeric() > bCAAA <- 0 > cCAAA <- 0 > MNCAAA <- 0 > dCAAB <- numeric() > bCAAB <- 0 > cCAAB <- 0 > MNCAAB <- 0 > dCAAC <- numeric() > bCAAC <- 0 > cCAAC <- 0 > MNCAAC <- 0 > dCAAD <- numeric() > bCAAD <- 0 > cCAAD <- 0 > MNCAAD <- 0 > dCAAE <- numeric() > bCAAE <- 0 > cCAAE <- 0 > MNCAAE <- 0 > dCAAF <- numeric() > bCAAF <- 0 > cCAAF <- 0 > MNCAAF <- 0 > dCAAG <- numeric() > bCAAG <- 0 > cCAAG <- 0 > MNCAAG <- 0 > dCAAH <- numeric() > bCAAH <- 0 > cCAAH <- 0 > MNCAAH <- 0 > dCABA <- numeric() > bCABA <- 0 > cCABA <- 0 > MNCABA <- 0 > dCABB <- numeric() > bCABB <- 0 > cCABB <- 0 > MNCABB <- 0 > dCABC <- numeric() > bCABC <- 0 > cCABC <- 0 > MNCABC <- 0 > dCABD <- numeric() > bCABD <- 0 > cCABD <- 0 > MNCABD <- 0 > dCABE <- numeric() > bCABE <- 0 > cCABE <- 0 > MNCABE <- 0 > dCABF <- numeric() > bCABF <- 0 > cCABF <- 0 > MNCABF <- 0 > dCABG <- numeric() > bCABG <- 0 > cCABG <- 0 > MNCABG <- 0 > dCABH <- numeric() > bCABH <- 0 > cCABH <- 0 > MNCABH <- 0 > dCACA <- numeric() > bCACA <- 0 > cCACA <- 0 > MNCACA <- 0 > dCACB <- numeric() > bCACB <- 0 > cCACB <- 0 > MNCACB <- 0 > dCACC <- numeric() > bCACC <- 0 > cCACC <- 0 > MNCACC <- 0 > dCACD <- numeric() > bCACD <- 0 > cCACD <- 0 > MNCACD <- 0 > dCACE <- numeric() > bCACE <- 0 > cCACE <- 0 > MNCACE <- 0 > dCACF <- numeric() > bCACF <- 0 > cCACF <- 0 > MNCACF <- 0 > dCACG <- numeric() > bCACG <- 0 > cCACG <- 0 > MNCACG <- 0 > dCACH <- numeric() > bCACH <- 0 > cCACH <- 0 > MNCACH <- 0 > dCBAA <- numeric() > bCBAA <- 0 > cCBAA <- 0 > MNCBAA <- 0 > dCBAB <- numeric() > bCBAB <- 0 > cCBAB <- 0 > MNCBAB <- 0 > dCBAC <- numeric() > bCBAC <- 0 > cCBAC <- 0 > MNCBAC <- 0 > dCBAD <- numeric() > bCBAD <- 0 > cCBAD <- 0 > MNCBAD <- 0 > dCBAE <- numeric() > bCBAE <- 0 > cCBAE <- 0 > MNCBAE <- 0 > dCBAF <- numeric() > bCBAF <- 0 > cCBAF <- 0 > MNCBAF <- 0 > dCBAG <- numeric() > bCBAG <- 0 > cCBAG <- 0 > MNCBAG <- 0 > dCBAH <- numeric() > bCBAH <- 0 > cCBAH <- 0 > MNCBAH <- 0 > dCBBA <- numeric() > bCBBA <- 0 > cCBBA <- 0 > MNCBBA <- 0 > dCBBB <- numeric() > bCBBB <- 0 > cCBBB <- 0 > MNCBBB <- 0 > dCBBC <- numeric() > bCBBC <- 0 > cCBBC <- 0 > MNCBBC <- 0 > dCBBD <- numeric() > bCBBD <- 0 > cCBBD <- 0 > MNCBBD <- 0 > dCBBE <- numeric() > bCBBE <- 0 > cCBBE <- 0 > MNCBBE <- 0 > dCBBF <- numeric() > bCBBF <- 0 > cCBBF <- 0 > MNCBBF <- 0 > dCBBG <- numeric() > bCBBG <- 0 > cCBBG <- 0 > MNCBBG <- 0 > dCBBH <- numeric() > bCBBH <- 0 > cCBBH <- 0 > MNCBBH <- 0 > dCBCA <- numeric() > bCBCA <- 0 > cCBCA <- 0 > MNCBCA <- 0 > dCBCB <- numeric() > bCBCB <- 0 > cCBCB <- 0 > MNCBCB <- 0 > dCBCC <- numeric() > bCBCC <- 0 > cCBCC <- 0 > MNCBCC <- 0 > dCBCD <- numeric() > bCBCD <- 0 > cCBCD <- 0 > MNCBCD <- 0 > dCBCE <- numeric() > bCBCE <- 0 > cCBCE <- 0 > MNCBCE <- 0 > dCBCF <- numeric() > bCBCF <- 0 > cCBCF <- 0 > MNCBCF <- 0 > dCBCG <- numeric() > bCBCG <- 0 > cCBCG <- 0 > MNCBCG <- 0 > dCBCH <- numeric() > bCBCH <- 0 > cCBCH <- 0 > MNCBCH <- 0 > dCCAA <- numeric() > bCCAA <- 0 > cCCAA <- 0 > MNCCAA <- 0 > dCCAB <- numeric() > bCCAB <- 0 > cCCAB <- 0 > MNCCAB <- 0 > dCCAC <- numeric() > bCCAC <- 0 > cCCAC <- 0 > MNCCAC <- 0 > dCCAD <- numeric() > bCCAD <- 0 > cCCAD <- 0 > MNCCAD <- 0 > dCCAE <- numeric() > bCCAE <- 0 > cCCAE <- 0 > MNCCAE <- 0 > dCCAF <- numeric() > bCCAF <- 0 > cCCAF <- 0 > MNCCAF <- 0 > dCCAG <- numeric() > bCCAG <- 0 > cCCAG <- 0 > MNCCAG <- 0 > dCCAH <- numeric() > bCCAH <- 0 > cCCAH <- 0 > MNCCAH <- 0 > dCCBA <- numeric() > bCCBA <- 0 > cCCBA <- 0 > MNCCBA <- 0 > dCCBB <- numeric() > bCCBB <- 0 > cCCBB <- 0 > MNCCBB <- 0 > dCCBC <- numeric() > bCCBC <- 0 > cCCBC <- 0 > MNCCBC <- 0 > dCCBD <- numeric() > bCCBD <- 0 > cCCBD <- 0 > MNCCBD <- 0 > dCCBE <- numeric() > bCCBE <- 0 > cCCBE <- 0 > MNCCBE <- 0 > dCCBF <- numeric() > bCCBF <- 0 > cCCBF <- 0 > MNCCBF <- 0 > dCCBG <- numeric() > bCCBG <- 0 > cCCBG <- 0 > MNCCBG <- 0 > dCCBH <- numeric() > bCCBH <- 0 > cCCBH <- 0 > MNCCBH <- 0 > dCCCA <- numeric() > bCCCA <- 0 > cCCCA <- 0 > MNCCCA <- 0 > dCCCB <- numeric() > bCCCB <- 0 > cCCCB <- 0 > MNCCCB <- 0 > dCCCC <- numeric() > bCCCC <- 0 > cCCCC <- 0 > MNCCCC <- 0 > dCCCD <- numeric() > bCCCD <- 0 > cCCCD <- 0 > MNCCCD <- 0 > dCCCE <- numeric() > bCCCE <- 0 > cCCCE <- 0 > MNCCCE <- 0 > dCCCF <- numeric() > bCCCF <- 0 > cCCCF <- 0 > MNCCCF <- 0 > dCCCG <- numeric() > bCCCG <- 0 > cCCCG <- 0 > MNCCCG <- 0 > dCCCH <- numeric() > bCCCH <- 0 > cCCCH <- 0 > MNCCCH <- 0 > dCDAA <- numeric() > bCDAA <- 0 > cCDAA <- 0 > MNCDAA <- 0 > dCDAB <- numeric() > bCDAB <- 0 > cCDAB <- 0 > MNCDAB <- 0 > dCDAC <- numeric() > bCDAC <- 0 > cCDAC <- 0 > MNCDAC <- 0 > dCDAD <- numeric() > bCDAD <- 0 > cCDAD <- 0 > MNCDAD <- 0 > dCDAE <- numeric() > bCDAE <- 0 > cCDAE <- 0 > MNCDAE <- 0 > dCDAF <- numeric() > bCDAF <- 0 > cCDAF <- 0 > MNCDAF <- 0 > dCDAG <- numeric() > bCDAG <- 0 > cCDAG <- 0 > MNCDAG <- 0 > dCDAH <- numeric() > bCDAH <- 0 > cCDAH <- 0 > MNCDAH <- 0 > dCDBA <- numeric() > bCDBA <- 0 > cCDBA <- 0 > MNCDBA <- 0 > dCDBB <- numeric() > bCDBB <- 0 > cCDBB <- 0 > MNCDBB <- 0 > dCDBC <- numeric() > bCDBC <- 0 > cCDBC <- 0 > MNCDBC <- 0 > dCDBD <- numeric() > bCDBD <- 0 > cCDBD <- 0 > MNCDBD <- 0 > dCDBE <- numeric() > bCDBE <- 0 > cCDBE <- 0 > MNCDBE <- 0 > dCDBF <- numeric() > bCDBF <- 0 > cCDBF <- 0 > MNCDBF <- 0 > dCDBG <- numeric() > bCDBG <- 0 > cCDBG <- 0 > MNCDBG <- 0 > dCDBH <- numeric() > bCDBH <- 0 > cCDBH <- 0 > MNCDBH <- 0 > dCDCA <- numeric() > bCDCA <- 0 > cCDCA <- 0 > MNCDCA <- 0 > dCDCB <- numeric() > bCDCB <- 0 > cCDCB <- 0 > MNCDCB <- 0 > dCDCC <- numeric() > bCDCC <- 0 > cCDCC <- 0 > MNCDCC <- 0 > dCDCD <- numeric() > bCDCD <- 0 > cCDCD <- 0 > MNCDCD <- 0 > dCDCE <- numeric() > bCDCE <- 0 > cCDCE <- 0 > MNCDCE <- 0 > dCDCF <- numeric() > bCDCF <- 0 > cCDCF <- 0 > MNCDCF <- 0 > dCDCG <- numeric() > bCDCG <- 0 > cCDCG <- 0 > MNCDCG <- 0 > dCDCH <- numeric() > bCDCH <- 0 > cCDCH <- 0 > MNCDCH <- 0 > dCEAA <- numeric() > bCEAA <- 0 > cCEAA <- 0 > MNCEAA <- 0 > dCEAB <- numeric() > bCEAB <- 0 > cCEAB <- 0 > MNCEAB <- 0 > dCEAC <- numeric() > bCEAC <- 0 > cCEAC <- 0 > MNCEAC <- 0 > dCEAD <- numeric() > bCEAD <- 0 > cCEAD <- 0 > MNCEAD <- 0 > dCEAE <- numeric() > bCEAE <- 0 > cCEAE <- 0 > MNCEAE <- 0 > dCEAF <- numeric() > bCEAF <- 0 > cCEAF <- 0 > MNCEAF <- 0 > dCEAG <- numeric() > bCEAG <- 0 > cCEAG <- 0 > MNCEAG <- 0 > dCEAH <- numeric() > bCEAH <- 0 > cCEAH <- 0 > MNCEAH <- 0 > dCEBA <- numeric() > bCEBA <- 0 > cCEBA <- 0 > MNCEBA <- 0 > dCEBB <- numeric() > bCEBB <- 0 > cCEBB <- 0 > MNCEBB <- 0 > dCEBC <- numeric() > bCEBC <- 0 > cCEBC <- 0 > MNCEBC <- 0 > dCEBD <- numeric() > bCEBD <- 0 > cCEBD <- 0 > MNCEBD <- 0 > dCEBE <- numeric() > bCEBE <- 0 > cCEBE <- 0 > MNCEBE <- 0 > dCEBF <- numeric() > bCEBF <- 0 > cCEBF <- 0 > MNCEBF <- 0 > dCEBG <- numeric() > bCEBG <- 0 > cCEBG <- 0 > MNCEBG <- 0 > dCEBH <- numeric() > bCEBH <- 0 > cCEBH <- 0 > MNCEBH <- 0 > dCECA <- numeric() > bCECA <- 0 > cCECA <- 0 > MNCECA <- 0 > dCECB <- numeric() > bCECB <- 0 > cCECB <- 0 > MNCECB <- 0 > dCECC <- numeric() > bCECC <- 0 > cCECC <- 0 > MNCECC <- 0 > dCECD <- numeric() > bCECD <- 0 > cCECD <- 0 > MNCECD <- 0 > dCECE <- numeric() > bCECE <- 0 > cCECE <- 0 > MNCECE <- 0 > dCECF <- numeric() > bCECF <- 0 > cCECF <- 0 > MNCECF <- 0 > dCECG <- numeric() > bCECG <- 0 > cCECG <- 0 > MNCECG <- 0 > dCECH <- numeric() > bCECH <- 0 > cCECH <- 0 > MNCECH <- 0 > dCFAA <- numeric() > bCFAA <- 0 > cCFAA <- 0 > MNCFAA <- 0 > dCFAB <- numeric() > bCFAB <- 0 > cCFAB <- 0 > MNCFAB <- 0 > dCFAC <- numeric() > bCFAC <- 0 > cCFAC <- 0 > MNCFAC <- 0 > dCFAD <- numeric() > bCFAD <- 0 > cCFAD <- 0 > MNCFAD <- 0 > dCFAE <- numeric() > bCFAE <- 0 > cCFAE <- 0 > MNCFAE <- 0 > dCFAF <- numeric() > bCFAF <- 0 > cCFAF <- 0 > MNCFAF <- 0 > dCFAG <- numeric() > bCFAG <- 0 > cCFAG <- 0 > MNCFAG <- 0 > dCFAH <- numeric() > bCFAH <- 0 > cCFAH <- 0 > MNCFAH <- 0 > dCFBA <- numeric() > bCFBA <- 0 > cCFBA <- 0 > MNCFBA <- 0 > dCFBB <- numeric() > bCFBB <- 0 > cCFBB <- 0 > MNCFBB <- 0 > dCFBC <- numeric() > bCFBC <- 0 > cCFBC <- 0 > MNCFBC <- 0 > dCFBD <- numeric() > bCFBD <- 0 > cCFBD <- 0 > MNCFBD <- 0 > dCFBE <- numeric() > bCFBE <- 0 > cCFBE <- 0 > MNCFBE <- 0 > dCFBF <- numeric() > bCFBF <- 0 > cCFBF <- 0 > MNCFBF <- 0 > dCFBG <- numeric() > bCFBG <- 0 > cCFBG <- 0 > MNCFBG <- 0 > dCFBH <- numeric() > bCFBH <- 0 > cCFBH <- 0 > MNCFBH <- 0 > dCFCA <- numeric() > bCFCA <- 0 > cCFCA <- 0 > MNCFCA <- 0 > dCFCB <- numeric() > bCFCB <- 0 > cCFCB <- 0 > MNCFCB <- 0 > dCFCC <- numeric() > bCFCC <- 0 > cCFCC <- 0 > MNCFCC <- 0 > dCFCD <- numeric() > bCFCD <- 0 > cCFCD <- 0 > MNCFCD <- 0 > dCFCE <- numeric() > bCFCE <- 0 > cCFCE <- 0 > MNCFCE <- 0 > dCFCF <- numeric() > bCFCF <- 0 > cCFCF <- 0 > MNCFCF <- 0 > dCFCG <- numeric() > bCFCG <- 0 > cCFCG <- 0 > MNCFCG <- 0 > dCFCH <- numeric() > bCFCH <- 0 > cCFCH <- 0 > MNCFCH <- 0 > dCGAA <- numeric() > bCGAA <- 0 > cCGAA <- 0 > MNCGAA <- 0 > dCGAB <- numeric() > bCGAB <- 0 > cCGAB <- 0 > MNCGAB <- 0 > dCGAC <- numeric() > bCGAC <- 0 > cCGAC <- 0 > MNCGAC <- 0 > dCGAD <- numeric() > bCGAD <- 0 > cCGAD <- 0 > MNCGAD <- 0 > dCGAE <- numeric() > bCGAE <- 0 > cCGAE <- 0 > MNCGAE <- 0 > dCGAF <- numeric() > bCGAF <- 0 > cCGAF <- 0 > MNCGAF <- 0 > dCGAG <- numeric() > bCGAG <- 0 > cCGAG <- 0 > MNCGAG <- 0 > dCGAH <- numeric() > bCGAH <- 0 > cCGAH <- 0 > MNCGAH <- 0 > dCGBA <- numeric() > bCGBA <- 0 > cCGBA <- 0 > MNCGBA <- 0 > dCGBB <- numeric() > bCGBB <- 0 > cCGBB <- 0 > MNCGBB <- 0 > dCGBC <- numeric() > bCGBC <- 0 > cCGBC <- 0 > MNCGBC <- 0 > dCGBD <- numeric() > bCGBD <- 0 > cCGBD <- 0 > MNCGBD <- 0 > dCGBE <- numeric() > bCGBE <- 0 > cCGBE <- 0 > MNCGBE <- 0 > dCGBF <- numeric() > bCGBF <- 0 > cCGBF <- 0 > MNCGBF <- 0 > dCGBG <- numeric() > bCGBG <- 0 > cCGBG <- 0 > MNCGBG <- 0 > dCGBH <- numeric() > bCGBH <- 0 > cCGBH <- 0 > MNCGBH <- 0 > dCGCA <- numeric() > bCGCA <- 0 > cCGCA <- 0 > MNCGCA <- 0 > dCGCB <- numeric() > bCGCB <- 0 > cCGCB <- 0 > MNCGCB <- 0 > dCGCC <- numeric() > bCGCC <- 0 > cCGCC <- 0 > MNCGCC <- 0 > dCGCD <- numeric() > bCGCD <- 0 > cCGCD <- 0 > MNCGCD <- 0 > dCGCE <- numeric() > bCGCE <- 0 > cCGCE <- 0 > MNCGCE <- 0 > dCGCF <- numeric() > bCGCF <- 0 > cCGCF <- 0 > MNCGCF <- 0 > dCGCG <- numeric() > bCGCG <- 0 > cCGCG <- 0 > MNCGCG <- 0 > dCGCH <- numeric() > bCGCH <- 0 > cCGCH <- 0 > MNCGCH <- 0 > dCHAA <- numeric() > bCHAA <- 0 > cCHAA <- 0 > MNCHAA <- 0 > dCHAB <- numeric() > bCHAB <- 0 > cCHAB <- 0 > MNCHAB <- 0 > dCHAC <- numeric() > bCHAC <- 0 > cCHAC <- 0 > MNCHAC <- 0 > dCHAD <- numeric() > bCHAD <- 0 > cCHAD <- 0 > MNCHAD <- 0 > dCHAE <- numeric() > bCHAE <- 0 > cCHAE <- 0 > MNCHAE <- 0 > dCHAF <- numeric() > bCHAF <- 0 > cCHAF <- 0 > MNCHAF <- 0 > dCHAG <- numeric() > bCHAG <- 0 > cCHAG <- 0 > MNCHAG <- 0 > dCHAH <- numeric() > bCHAH <- 0 > cCHAH <- 0 > MNCHAH <- 0 > dCHBA <- numeric() > bCHBA <- 0 > cCHBA <- 0 > MNCHBA <- 0 > dCHBB <- numeric() > bCHBB <- 0 > cCHBB <- 0 > MNCHBB <- 0 > dCHBC <- numeric() > bCHBC <- 0 > cCHBC <- 0 > MNCHBC <- 0 > dCHBD <- numeric() > bCHBD <- 0 > cCHBD <- 0 > MNCHBD <- 0 > dCHBE <- numeric() > bCHBE <- 0 > cCHBE <- 0 > MNCHBE <- 0 > dCHBF <- numeric() > bCHBF <- 0 > cCHBF <- 0 > MNCHBF <- 0 > dCHBG <- numeric() > bCHBG <- 0 > cCHBG <- 0 > MNCHBG <- 0 > dCHBH <- numeric() > bCHBH <- 0 > cCHBH <- 0 > MNCHBH <- 0 > dCHCA <- numeric() > bCHCA <- 0 > cCHCA <- 0 > MNCHCA <- 0 > dCHCB <- numeric() > bCHCB <- 0 > cCHCB <- 0 > MNCHCB <- 0 > dCHCC <- numeric() > bCHCC <- 0 > cCHCC <- 0 > MNCHCC <- 0 > dCHCD <- numeric() > bCHCD <- 0 > cCHCD <- 0 > MNCHCD <- 0 > dCHCE <- numeric() > bCHCE <- 0 > cCHCE <- 0 > MNCHCE <- 0 > dCHCF <- numeric() > bCHCF <- 0 > cCHCF <- 0 > MNCHCF <- 0 > dCHCG <- numeric() > bCHCG <- 0 > cCHCG <- 0 > MNCHCG <- 0 > dCHCH <- numeric() > bCHCH <- 0 > cCHCH <- 0 > MNCHCH <- 0 > > > > > > for(i in 1:length(AA)){ + if(AA[[i]] > AVEAAAA){ dAAAA[[i]] <- '0' } else {dAAAA[[i]] <- '1'} + if(AA[[i]] > AVEAAAA){ dAAAA[[i]] <- '0' } else {dAAAA[[i]] <- '1'} + if(AA[[i]] > AVEAAAB){ dAAAB[[i]] <- '0' } else {dAAAB[[i]] <- '1'} + if(AB[[i]] > AVEAAAB){ dABAA[[i]] <- '0' } else {dABAA[[i]] <- '1'} + if(AA[[i]] > AVEAAAC){ dAAAC[[i]] <- '0' } else {dAAAC[[i]] <- '1'} + if(AC[[i]] > AVEAAAC){ dACAA[[i]] <- '0' } else {dACAA[[i]] <- '1'} + if(AA[[i]] > AVEAAAD){ dAAAD[[i]] <- '0' } else {dAAAD[[i]] <- '1'} + if(AD[[i]] > AVEAAAD){ dADAA[[i]] <- '0' } else {dADAA[[i]] <- '1'} + if(AA[[i]] > AVEAAAE){ dAAAE[[i]] <- '0' } else {dAAAE[[i]] <- '1'} + if(AE[[i]] > AVEAAAE){ dAEAA[[i]] <- '0' } else {dAEAA[[i]] <- '1'} + if(AA[[i]] > AVEAAAF){ dAAAF[[i]] <- '0' } else {dAAAF[[i]] <- '1'} + if(AF[[i]] > AVEAAAF){ dAFAA[[i]] <- '0' } else {dAFAA[[i]] <- '1'} + if(AA[[i]] > AVEAAAG){ dAAAG[[i]] <- '0' } else {dAAAG[[i]] <- '1'} + if(AG[[i]] > AVEAAAG){ dAGAA[[i]] <- '0' } else {dAGAA[[i]] <- '1'} + if(AA[[i]] > AVEAAAH){ dAAAH[[i]] <- '0' } else {dAAAH[[i]] <- '1'} + if(AH[[i]] > AVEAAAH){ dAHAA[[i]] <- '0' } else {dAHAA[[i]] <- '1'} + if(AA[[i]] > AVEAABA){ dAABA[[i]] <- '0' } else {dAABA[[i]] <- '1'} + if(BA[[i]] > AVEAABA){ dBAAA[[i]] <- '0' } else {dBAAA[[i]] <- '1'} + if(AA[[i]] > AVEAABB){ dAABB[[i]] <- '0' } else {dAABB[[i]] <- '1'} + if(BB[[i]] > AVEAABB){ dBBAA[[i]] <- '0' } else {dBBAA[[i]] <- '1'} + if(AA[[i]] > AVEAABC){ dAABC[[i]] <- '0' } else {dAABC[[i]] <- '1'} + if(BC[[i]] > AVEAABC){ dBCAA[[i]] <- '0' } else {dBCAA[[i]] <- '1'} + if(AA[[i]] > AVEAABD){ dAABD[[i]] <- '0' } else {dAABD[[i]] <- '1'} + if(BD[[i]] > AVEAABD){ dBDAA[[i]] <- '0' } else {dBDAA[[i]] <- '1'} + if(AA[[i]] > AVEAABE){ dAABE[[i]] <- '0' } else {dAABE[[i]] <- '1'} + if(BE[[i]] > AVEAABE){ dBEAA[[i]] <- '0' } else {dBEAA[[i]] <- '1'} + if(AA[[i]] > AVEAABF){ dAABF[[i]] <- '0' } else {dAABF[[i]] <- '1'} + if(BF[[i]] > AVEAABF){ dBFAA[[i]] <- '0' } else {dBFAA[[i]] <- '1'} + if(AA[[i]] > AVEAABG){ dAABG[[i]] <- '0' } else {dAABG[[i]] <- '1'} + if(BG[[i]] > AVEAABG){ dBGAA[[i]] <- '0' } else {dBGAA[[i]] <- '1'} + if(AA[[i]] > AVEAABH){ dAABH[[i]] <- '0' } else {dAABH[[i]] <- '1'} + if(BH[[i]] > AVEAABH){ dBHAA[[i]] <- '0' } else {dBHAA[[i]] <- '1'} + if(AA[[i]] > AVEAACA){ dAACA[[i]] <- '0' } else {dAACA[[i]] <- '1'} + if(CA[[i]] > AVEAACA){ dCAAA[[i]] <- '0' } else {dCAAA[[i]] <- '1'} + if(AA[[i]] > AVEAACB){ dAACB[[i]] <- '0' } else {dAACB[[i]] <- '1'} + if(CB[[i]] > AVEAACB){ dCBAA[[i]] <- '0' } else {dCBAA[[i]] <- '1'} + if(AA[[i]] > AVEAACC){ dAACC[[i]] <- '0' } else {dAACC[[i]] <- '1'} + if(CC[[i]] > AVEAACC){ dCCAA[[i]] <- '0' } else {dCCAA[[i]] <- '1'} + if(AA[[i]] > AVEAACD){ dAACD[[i]] <- '0' } else {dAACD[[i]] <- '1'} + if(CD[[i]] > AVEAACD){ dCDAA[[i]] <- '0' } else {dCDAA[[i]] <- '1'} + if(AA[[i]] > AVEAACE){ dAACE[[i]] <- '0' } else {dAACE[[i]] <- '1'} + if(CE[[i]] > AVEAACE){ dCEAA[[i]] <- '0' } else {dCEAA[[i]] <- '1'} + if(AA[[i]] > AVEAACF){ dAACF[[i]] <- '0' } else {dAACF[[i]] <- '1'} + if(CF[[i]] > AVEAACF){ dCFAA[[i]] <- '0' } else {dCFAA[[i]] <- '1'} + if(AA[[i]] > AVEAACG){ dAACG[[i]] <- '0' } else {dAACG[[i]] <- '1'} + if(CG[[i]] > AVEAACG){ dCGAA[[i]] <- '0' } else {dCGAA[[i]] <- '1'} + if(AA[[i]] > AVEAACH){ dAACH[[i]] <- '0' } else {dAACH[[i]] <- '1'} + if(CH[[i]] > AVEAACH){ dCHAA[[i]] <- '0' } else {dCHAA[[i]] <- '1'} + if(AB[[i]] > AVEABAA){ dABAA[[i]] <- '0' } else {dABAA[[i]] <- '1'} + if(AA[[i]] > AVEABAA){ dAAAB[[i]] <- '0' } else {dAAAB[[i]] <- '1'} + if(AB[[i]] > AVEABAB){ dABAB[[i]] <- '0' } else {dABAB[[i]] <- '1'} + if(AB[[i]] > AVEABAB){ dABAB[[i]] <- '0' } else {dABAB[[i]] <- '1'} + if(AB[[i]] > AVEABAC){ dABAC[[i]] <- '0' } else {dABAC[[i]] <- '1'} + if(AC[[i]] > AVEABAC){ dACAB[[i]] <- '0' } else {dACAB[[i]] <- '1'} + if(AB[[i]] > AVEABAD){ dABAD[[i]] <- '0' } else {dABAD[[i]] <- '1'} + if(AD[[i]] > AVEABAD){ dADAB[[i]] <- '0' } else {dADAB[[i]] <- '1'} + if(AB[[i]] > AVEABAE){ dABAE[[i]] <- '0' } else {dABAE[[i]] <- '1'} + if(AE[[i]] > AVEABAE){ dAEAB[[i]] <- '0' } else {dAEAB[[i]] <- '1'} + if(AB[[i]] > AVEABAF){ dABAF[[i]] <- '0' } else {dABAF[[i]] <- '1'} + if(AF[[i]] > AVEABAF){ dAFAB[[i]] <- '0' } else {dAFAB[[i]] <- '1'} + if(AB[[i]] > AVEABAG){ dABAG[[i]] <- '0' } else {dABAG[[i]] <- '1'} + if(AG[[i]] > AVEABAG){ dAGAB[[i]] <- '0' } else {dAGAB[[i]] <- '1'} + if(AB[[i]] > AVEABAH){ dABAH[[i]] <- '0' } else {dABAH[[i]] <- '1'} + if(AH[[i]] > AVEABAH){ dAHAB[[i]] <- '0' } else {dAHAB[[i]] <- '1'} + if(AB[[i]] > AVEABBA){ dABBA[[i]] <- '0' } else {dABBA[[i]] <- '1'} + if(BA[[i]] > AVEABBA){ dBAAB[[i]] <- '0' } else {dBAAB[[i]] <- '1'} + if(AB[[i]] > AVEABBB){ dABBB[[i]] <- '0' } else {dABBB[[i]] <- '1'} + if(BB[[i]] > AVEABBB){ dBBAB[[i]] <- '0' } else {dBBAB[[i]] <- '1'} + if(AB[[i]] > AVEABBC){ dABBC[[i]] <- '0' } else {dABBC[[i]] <- '1'} + if(BC[[i]] > AVEABBC){ dBCAB[[i]] <- '0' } else {dBCAB[[i]] <- '1'} + if(AB[[i]] > AVEABBD){ dABBD[[i]] <- '0' } else {dABBD[[i]] <- '1'} + if(BD[[i]] > AVEABBD){ dBDAB[[i]] <- '0' } else {dBDAB[[i]] <- '1'} + if(AB[[i]] > AVEABBE){ dABBE[[i]] <- '0' } else {dABBE[[i]] <- '1'} + if(BE[[i]] > AVEABBE){ dBEAB[[i]] <- '0' } else {dBEAB[[i]] <- '1'} + if(AB[[i]] > AVEABBF){ dABBF[[i]] <- '0' } else {dABBF[[i]] <- '1'} + if(BF[[i]] > AVEABBF){ dBFAB[[i]] <- '0' } else {dBFAB[[i]] <- '1'} + if(AB[[i]] > AVEABBG){ dABBG[[i]] <- '0' } else {dABBG[[i]] <- '1'} + if(BG[[i]] > AVEABBG){ dBGAB[[i]] <- '0' } else {dBGAB[[i]] <- '1'} + if(AB[[i]] > AVEABBH){ dABBH[[i]] <- '0' } else {dABBH[[i]] <- '1'} + if(BH[[i]] > AVEABBH){ dBHAB[[i]] <- '0' } else {dBHAB[[i]] <- '1'} + if(AB[[i]] > AVEABCA){ dABCA[[i]] <- '0' } else {dABCA[[i]] <- '1'} + if(CA[[i]] > AVEABCA){ dCAAB[[i]] <- '0' } else {dCAAB[[i]] <- '1'} + if(AB[[i]] > AVEABCB){ dABCB[[i]] <- '0' } else {dABCB[[i]] <- '1'} + if(CB[[i]] > AVEABCB){ dCBAB[[i]] <- '0' } else {dCBAB[[i]] <- '1'} + if(AB[[i]] > AVEABCC){ dABCC[[i]] <- '0' } else {dABCC[[i]] <- '1'} + if(CC[[i]] > AVEABCC){ dCCAB[[i]] <- '0' } else {dCCAB[[i]] <- '1'} + if(AB[[i]] > AVEABCD){ dABCD[[i]] <- '0' } else {dABCD[[i]] <- '1'} + if(CD[[i]] > AVEABCD){ dCDAB[[i]] <- '0' } else {dCDAB[[i]] <- '1'} + if(AB[[i]] > AVEABCE){ dABCE[[i]] <- '0' } else {dABCE[[i]] <- '1'} + if(CE[[i]] > AVEABCE){ dCEAB[[i]] <- '0' } else {dCEAB[[i]] <- '1'} + if(AB[[i]] > AVEABCF){ dABCF[[i]] <- '0' } else {dABCF[[i]] <- '1'} + if(CF[[i]] > AVEABCF){ dCFAB[[i]] <- '0' } else {dCFAB[[i]] <- '1'} + if(AB[[i]] > AVEABCG){ dABCG[[i]] <- '0' } else {dABCG[[i]] <- '1'} + if(CG[[i]] > AVEABCG){ dCGAB[[i]] <- '0' } else {dCGAB[[i]] <- '1'} + if(AB[[i]] > AVEABCH){ dABCH[[i]] <- '0' } else {dABCH[[i]] <- '1'} + if(CH[[i]] > AVEABCH){ dCHAB[[i]] <- '0' } else {dCHAB[[i]] <- '1'} + if(AC[[i]] > AVEACAA){ dACAA[[i]] <- '0' } else {dACAA[[i]] <- '1'} + if(AA[[i]] > AVEACAA){ dAAAC[[i]] <- '0' } else {dAAAC[[i]] <- '1'} + if(AC[[i]] > AVEACAB){ dACAB[[i]] <- '0' } else {dACAB[[i]] <- '1'} + if(AB[[i]] > AVEACAB){ dABAC[[i]] <- '0' } else {dABAC[[i]] <- '1'} + if(AC[[i]] > AVEACAC){ dACAC[[i]] <- '0' } else {dACAC[[i]] <- '1'} + if(AC[[i]] > AVEACAC){ dACAC[[i]] <- '0' } else {dACAC[[i]] <- '1'} + if(AC[[i]] > AVEACAD){ dACAD[[i]] <- '0' } else {dACAD[[i]] <- '1'} + if(AD[[i]] > AVEACAD){ dADAC[[i]] <- '0' } else {dADAC[[i]] <- '1'} + if(AC[[i]] > AVEACAE){ dACAE[[i]] <- '0' } else {dACAE[[i]] <- '1'} + if(AE[[i]] > AVEACAE){ dAEAC[[i]] <- '0' } else {dAEAC[[i]] <- '1'} + if(AC[[i]] > AVEACAF){ dACAF[[i]] <- '0' } else {dACAF[[i]] <- '1'} + if(AF[[i]] > AVEACAF){ dAFAC[[i]] <- '0' } else {dAFAC[[i]] <- '1'} + if(AC[[i]] > AVEACAG){ dACAG[[i]] <- '0' } else {dACAG[[i]] <- '1'} + if(AG[[i]] > AVEACAG){ dAGAC[[i]] <- '0' } else {dAGAC[[i]] <- '1'} + if(AC[[i]] > AVEACAH){ dACAH[[i]] <- '0' } else {dACAH[[i]] <- '1'} + if(AH[[i]] > AVEACAH){ dAHAC[[i]] <- '0' } else {dAHAC[[i]] <- '1'} + if(AC[[i]] > AVEACBA){ dACBA[[i]] <- '0' } else {dACBA[[i]] <- '1'} + if(BA[[i]] > AVEACBA){ dBAAC[[i]] <- '0' } else {dBAAC[[i]] <- '1'} + if(AC[[i]] > AVEACBB){ dACBB[[i]] <- '0' } else {dACBB[[i]] <- '1'} + if(BB[[i]] > AVEACBB){ dBBAC[[i]] <- '0' } else {dBBAC[[i]] <- '1'} + if(AC[[i]] > AVEACBC){ dACBC[[i]] <- '0' } else {dACBC[[i]] <- '1'} + if(BC[[i]] > AVEACBC){ dBCAC[[i]] <- '0' } else {dBCAC[[i]] <- '1'} + if(AC[[i]] > AVEACBD){ dACBD[[i]] <- '0' } else {dACBD[[i]] <- '1'} + if(BD[[i]] > AVEACBD){ dBDAC[[i]] <- '0' } else {dBDAC[[i]] <- '1'} + if(AC[[i]] > AVEACBE){ dACBE[[i]] <- '0' } else {dACBE[[i]] <- '1'} + if(BE[[i]] > AVEACBE){ dBEAC[[i]] <- '0' } else {dBEAC[[i]] <- '1'} + if(AC[[i]] > AVEACBF){ dACBF[[i]] <- '0' } else {dACBF[[i]] <- '1'} + if(BF[[i]] > AVEACBF){ dBFAC[[i]] <- '0' } else {dBFAC[[i]] <- '1'} + if(AC[[i]] > AVEACBG){ dACBG[[i]] <- '0' } else {dACBG[[i]] <- '1'} + if(BG[[i]] > AVEACBG){ dBGAC[[i]] <- '0' } else {dBGAC[[i]] <- '1'} + if(AC[[i]] > AVEACBH){ dACBH[[i]] <- '0' } else {dACBH[[i]] <- '1'} + if(BH[[i]] > AVEACBH){ dBHAC[[i]] <- '0' } else {dBHAC[[i]] <- '1'} + if(AC[[i]] > AVEACCA){ dACCA[[i]] <- '0' } else {dACCA[[i]] <- '1'} + if(CA[[i]] > AVEACCA){ dCAAC[[i]] <- '0' } else {dCAAC[[i]] <- '1'} + if(AC[[i]] > AVEACCB){ dACCB[[i]] <- '0' } else {dACCB[[i]] <- '1'} + if(CB[[i]] > AVEACCB){ dCBAC[[i]] <- '0' } else {dCBAC[[i]] <- '1'} + if(AC[[i]] > AVEACCC){ dACCC[[i]] <- '0' } else {dACCC[[i]] <- '1'} + if(CC[[i]] > AVEACCC){ dCCAC[[i]] <- '0' } else {dCCAC[[i]] <- '1'} + if(AC[[i]] > AVEACCD){ dACCD[[i]] <- '0' } else {dACCD[[i]] <- '1'} + if(CD[[i]] > AVEACCD){ dCDAC[[i]] <- '0' } else {dCDAC[[i]] <- '1'} + if(AC[[i]] > AVEACCE){ dACCE[[i]] <- '0' } else {dACCE[[i]] <- '1'} + if(CE[[i]] > AVEACCE){ dCEAC[[i]] <- '0' } else {dCEAC[[i]] <- '1'} + if(AC[[i]] > AVEACCF){ dACCF[[i]] <- '0' } else {dACCF[[i]] <- '1'} + if(CF[[i]] > AVEACCF){ dCFAC[[i]] <- '0' } else {dCFAC[[i]] <- '1'} + if(AC[[i]] > AVEACCG){ dACCG[[i]] <- '0' } else {dACCG[[i]] <- '1'} + if(CG[[i]] > AVEACCG){ dCGAC[[i]] <- '0' } else {dCGAC[[i]] <- '1'} + if(AC[[i]] > AVEACCH){ dACCH[[i]] <- '0' } else {dACCH[[i]] <- '1'} + if(CH[[i]] > AVEACCH){ dCHAC[[i]] <- '0' } else {dCHAC[[i]] <- '1'} + if(AD[[i]] > AVEADAA){ dADAA[[i]] <- '0' } else {dADAA[[i]] <- '1'} + if(AA[[i]] > AVEADAA){ dAAAD[[i]] <- '0' } else {dAAAD[[i]] <- '1'} + if(AD[[i]] > AVEADAB){ dADAB[[i]] <- '0' } else {dADAB[[i]] <- '1'} + if(AB[[i]] > AVEADAB){ dABAD[[i]] <- '0' } else {dABAD[[i]] <- '1'} + if(AD[[i]] > AVEADAC){ dADAC[[i]] <- '0' } else {dADAC[[i]] <- '1'} + if(AC[[i]] > AVEADAC){ dACAD[[i]] <- '0' } else {dACAD[[i]] <- '1'} + if(AD[[i]] > AVEADAD){ dADAD[[i]] <- '0' } else {dADAD[[i]] <- '1'} + if(AD[[i]] > AVEADAD){ dADAD[[i]] <- '0' } else {dADAD[[i]] <- '1'} + if(AD[[i]] > AVEADAE){ dADAE[[i]] <- '0' } else {dADAE[[i]] <- '1'} + if(AE[[i]] > AVEADAE){ dAEAD[[i]] <- '0' } else {dAEAD[[i]] <- '1'} + if(AD[[i]] > AVEADAF){ dADAF[[i]] <- '0' } else {dADAF[[i]] <- '1'} + if(AF[[i]] > AVEADAF){ dAFAD[[i]] <- '0' } else {dAFAD[[i]] <- '1'} + if(AD[[i]] > AVEADAG){ dADAG[[i]] <- '0' } else {dADAG[[i]] <- '1'} + if(AG[[i]] > AVEADAG){ dAGAD[[i]] <- '0' } else {dAGAD[[i]] <- '1'} + if(AD[[i]] > AVEADAH){ dADAH[[i]] <- '0' } else {dADAH[[i]] <- '1'} + if(AH[[i]] > AVEADAH){ dAHAD[[i]] <- '0' } else {dAHAD[[i]] <- '1'} + if(AD[[i]] > AVEADBA){ dADBA[[i]] <- '0' } else {dADBA[[i]] <- '1'} + if(BA[[i]] > AVEADBA){ dBAAD[[i]] <- '0' } else {dBAAD[[i]] <- '1'} + if(AD[[i]] > AVEADBB){ dADBB[[i]] <- '0' } else {dADBB[[i]] <- '1'} + if(BB[[i]] > AVEADBB){ dBBAD[[i]] <- '0' } else {dBBAD[[i]] <- '1'} + if(AD[[i]] > AVEADBC){ dADBC[[i]] <- '0' } else {dADBC[[i]] <- '1'} + if(BC[[i]] > AVEADBC){ dBCAD[[i]] <- '0' } else {dBCAD[[i]] <- '1'} + if(AD[[i]] > AVEADBD){ dADBD[[i]] <- '0' } else {dADBD[[i]] <- '1'} + if(BD[[i]] > AVEADBD){ dBDAD[[i]] <- '0' } else {dBDAD[[i]] <- '1'} + if(AD[[i]] > AVEADBE){ dADBE[[i]] <- '0' } else {dADBE[[i]] <- '1'} + if(BE[[i]] > AVEADBE){ dBEAD[[i]] <- '0' } else {dBEAD[[i]] <- '1'} + if(AD[[i]] > AVEADBF){ dADBF[[i]] <- '0' } else {dADBF[[i]] <- '1'} + if(BF[[i]] > AVEADBF){ dBFAD[[i]] <- '0' } else {dBFAD[[i]] <- '1'} + if(AD[[i]] > AVEADBG){ dADBG[[i]] <- '0' } else {dADBG[[i]] <- '1'} + if(BG[[i]] > AVEADBG){ dBGAD[[i]] <- '0' } else {dBGAD[[i]] <- '1'} + if(AD[[i]] > AVEADBH){ dADBH[[i]] <- '0' } else {dADBH[[i]] <- '1'} + if(BH[[i]] > AVEADBH){ dBHAD[[i]] <- '0' } else {dBHAD[[i]] <- '1'} + if(AD[[i]] > AVEADCA){ dADCA[[i]] <- '0' } else {dADCA[[i]] <- '1'} + if(CA[[i]] > AVEADCA){ dCAAD[[i]] <- '0' } else {dCAAD[[i]] <- '1'} + if(AD[[i]] > AVEADCB){ dADCB[[i]] <- '0' } else {dADCB[[i]] <- '1'} + if(CB[[i]] > AVEADCB){ dCBAD[[i]] <- '0' } else {dCBAD[[i]] <- '1'} + if(AD[[i]] > AVEADCC){ dADCC[[i]] <- '0' } else {dADCC[[i]] <- '1'} + if(CC[[i]] > AVEADCC){ dCCAD[[i]] <- '0' } else {dCCAD[[i]] <- '1'} + if(AD[[i]] > AVEADCD){ dADCD[[i]] <- '0' } else {dADCD[[i]] <- '1'} + if(CD[[i]] > AVEADCD){ dCDAD[[i]] <- '0' } else {dCDAD[[i]] <- '1'} + if(AD[[i]] > AVEADCE){ dADCE[[i]] <- '0' } else {dADCE[[i]] <- '1'} + if(CE[[i]] > AVEADCE){ dCEAD[[i]] <- '0' } else {dCEAD[[i]] <- '1'} + if(AD[[i]] > AVEADCF){ dADCF[[i]] <- '0' } else {dADCF[[i]] <- '1'} + if(CF[[i]] > AVEADCF){ dCFAD[[i]] <- '0' } else {dCFAD[[i]] <- '1'} + if(AD[[i]] > AVEADCG){ dADCG[[i]] <- '0' } else {dADCG[[i]] <- '1'} + if(CG[[i]] > AVEADCG){ dCGAD[[i]] <- '0' } else {dCGAD[[i]] <- '1'} + if(AD[[i]] > AVEADCH){ dADCH[[i]] <- '0' } else {dADCH[[i]] <- '1'} + if(CH[[i]] > AVEADCH){ dCHAD[[i]] <- '0' } else {dCHAD[[i]] <- '1'} + if(AE[[i]] > AVEAEAA){ dAEAA[[i]] <- '0' } else {dAEAA[[i]] <- '1'} + if(AA[[i]] > AVEAEAA){ dAAAE[[i]] <- '0' } else {dAAAE[[i]] <- '1'} + if(AE[[i]] > AVEAEAB){ dAEAB[[i]] <- '0' } else {dAEAB[[i]] <- '1'} + if(AB[[i]] > AVEAEAB){ dABAE[[i]] <- '0' } else {dABAE[[i]] <- '1'} + if(AE[[i]] > AVEAEAC){ dAEAC[[i]] <- '0' } else {dAEAC[[i]] <- '1'} + if(AC[[i]] > AVEAEAC){ dACAE[[i]] <- '0' } else {dACAE[[i]] <- '1'} + if(AE[[i]] > AVEAEAD){ dAEAD[[i]] <- '0' } else {dAEAD[[i]] <- '1'} + if(AD[[i]] > AVEAEAD){ dADAE[[i]] <- '0' } else {dADAE[[i]] <- '1'} + if(AE[[i]] > AVEAEAE){ dAEAE[[i]] <- '0' } else {dAEAE[[i]] <- '1'} + if(AE[[i]] > AVEAEAE){ dAEAE[[i]] <- '0' } else {dAEAE[[i]] <- '1'} + if(AE[[i]] > AVEAEAF){ dAEAF[[i]] <- '0' } else {dAEAF[[i]] <- '1'} + if(AF[[i]] > AVEAEAF){ dAFAE[[i]] <- '0' } else {dAFAE[[i]] <- '1'} + if(AE[[i]] > AVEAEAG){ dAEAG[[i]] <- '0' } else {dAEAG[[i]] <- '1'} + if(AG[[i]] > AVEAEAG){ dAGAE[[i]] <- '0' } else {dAGAE[[i]] <- '1'} + if(AE[[i]] > AVEAEAH){ dAEAH[[i]] <- '0' } else {dAEAH[[i]] <- '1'} + if(AH[[i]] > AVEAEAH){ dAHAE[[i]] <- '0' } else {dAHAE[[i]] <- '1'} + if(AE[[i]] > AVEAEBA){ dAEBA[[i]] <- '0' } else {dAEBA[[i]] <- '1'} + if(BA[[i]] > AVEAEBA){ dBAAE[[i]] <- '0' } else {dBAAE[[i]] <- '1'} + if(AE[[i]] > AVEAEBB){ dAEBB[[i]] <- '0' } else {dAEBB[[i]] <- '1'} + if(BB[[i]] > AVEAEBB){ dBBAE[[i]] <- '0' } else {dBBAE[[i]] <- '1'} + if(AE[[i]] > AVEAEBC){ dAEBC[[i]] <- '0' } else {dAEBC[[i]] <- '1'} + if(BC[[i]] > AVEAEBC){ dBCAE[[i]] <- '0' } else {dBCAE[[i]] <- '1'} + if(AE[[i]] > AVEAEBD){ dAEBD[[i]] <- '0' } else {dAEBD[[i]] <- '1'} + if(BD[[i]] > AVEAEBD){ dBDAE[[i]] <- '0' } else {dBDAE[[i]] <- '1'} + if(AE[[i]] > AVEAEBE){ dAEBE[[i]] <- '0' } else {dAEBE[[i]] <- '1'} + if(BE[[i]] > AVEAEBE){ dBEAE[[i]] <- '0' } else {dBEAE[[i]] <- '1'} + if(AE[[i]] > AVEAEBF){ dAEBF[[i]] <- '0' } else {dAEBF[[i]] <- '1'} + if(BF[[i]] > AVEAEBF){ dBFAE[[i]] <- '0' } else {dBFAE[[i]] <- '1'} + if(AE[[i]] > AVEAEBG){ dAEBG[[i]] <- '0' } else {dAEBG[[i]] <- '1'} + if(BG[[i]] > AVEAEBG){ dBGAE[[i]] <- '0' } else {dBGAE[[i]] <- '1'} + if(AE[[i]] > AVEAEBH){ dAEBH[[i]] <- '0' } else {dAEBH[[i]] <- '1'} + if(BH[[i]] > AVEAEBH){ dBHAE[[i]] <- '0' } else {dBHAE[[i]] <- '1'} + if(AE[[i]] > AVEAECA){ dAECA[[i]] <- '0' } else {dAECA[[i]] <- '1'} + if(CA[[i]] > AVEAECA){ dCAAE[[i]] <- '0' } else {dCAAE[[i]] <- '1'} + if(AE[[i]] > AVEAECB){ dAECB[[i]] <- '0' } else {dAECB[[i]] <- '1'} + if(CB[[i]] > AVEAECB){ dCBAE[[i]] <- '0' } else {dCBAE[[i]] <- '1'} + if(AE[[i]] > AVEAECC){ dAECC[[i]] <- '0' } else {dAECC[[i]] <- '1'} + if(CC[[i]] > AVEAECC){ dCCAE[[i]] <- '0' } else {dCCAE[[i]] <- '1'} + if(AE[[i]] > AVEAECD){ dAECD[[i]] <- '0' } else {dAECD[[i]] <- '1'} + if(CD[[i]] > AVEAECD){ dCDAE[[i]] <- '0' } else {dCDAE[[i]] <- '1'} + if(AE[[i]] > AVEAECE){ dAECE[[i]] <- '0' } else {dAECE[[i]] <- '1'} + if(CE[[i]] > AVEAECE){ dCEAE[[i]] <- '0' } else {dCEAE[[i]] <- '1'} + if(AE[[i]] > AVEAECF){ dAECF[[i]] <- '0' } else {dAECF[[i]] <- '1'} + if(CF[[i]] > AVEAECF){ dCFAE[[i]] <- '0' } else {dCFAE[[i]] <- '1'} + if(AE[[i]] > AVEAECG){ dAECG[[i]] <- '0' } else {dAECG[[i]] <- '1'} + if(CG[[i]] > AVEAECG){ dCGAE[[i]] <- '0' } else {dCGAE[[i]] <- '1'} + if(AE[[i]] > AVEAECH){ dAECH[[i]] <- '0' } else {dAECH[[i]] <- '1'} + if(CH[[i]] > AVEAECH){ dCHAE[[i]] <- '0' } else {dCHAE[[i]] <- '1'} + if(AF[[i]] > AVEAFAA){ dAFAA[[i]] <- '0' } else {dAFAA[[i]] <- '1'} + if(AA[[i]] > AVEAFAA){ dAAAF[[i]] <- '0' } else {dAAAF[[i]] <- '1'} + if(AF[[i]] > AVEAFAB){ dAFAB[[i]] <- '0' } else {dAFAB[[i]] <- '1'} + if(AB[[i]] > AVEAFAB){ dABAF[[i]] <- '0' } else {dABAF[[i]] <- '1'} + if(AF[[i]] > AVEAFAC){ dAFAC[[i]] <- '0' } else {dAFAC[[i]] <- '1'} + if(AC[[i]] > AVEAFAC){ dACAF[[i]] <- '0' } else {dACAF[[i]] <- '1'} + if(AF[[i]] > AVEAFAD){ dAFAD[[i]] <- '0' } else {dAFAD[[i]] <- '1'} + if(AD[[i]] > AVEAFAD){ dADAF[[i]] <- '0' } else {dADAF[[i]] <- '1'} + if(AF[[i]] > AVEAFAE){ dAFAE[[i]] <- '0' } else {dAFAE[[i]] <- '1'} + if(AE[[i]] > AVEAFAE){ dAEAF[[i]] <- '0' } else {dAEAF[[i]] <- '1'} + if(AF[[i]] > AVEAFAF){ dAFAF[[i]] <- '0' } else {dAFAF[[i]] <- '1'} + if(AF[[i]] > AVEAFAF){ dAFAF[[i]] <- '0' } else {dAFAF[[i]] <- '1'} + if(AF[[i]] > AVEAFAG){ dAFAG[[i]] <- '0' } else {dAFAG[[i]] <- '1'} + if(AG[[i]] > AVEAFAG){ dAGAF[[i]] <- '0' } else {dAGAF[[i]] <- '1'} + if(AF[[i]] > AVEAFAH){ dAFAH[[i]] <- '0' } else {dAFAH[[i]] <- '1'} + if(AH[[i]] > AVEAFAH){ dAHAF[[i]] <- '0' } else {dAHAF[[i]] <- '1'} + if(AF[[i]] > AVEAFBA){ dAFBA[[i]] <- '0' } else {dAFBA[[i]] <- '1'} + if(BA[[i]] > AVEAFBA){ dBAAF[[i]] <- '0' } else {dBAAF[[i]] <- '1'} + if(AF[[i]] > AVEAFBB){ dAFBB[[i]] <- '0' } else {dAFBB[[i]] <- '1'} + if(BB[[i]] > AVEAFBB){ dBBAF[[i]] <- '0' } else {dBBAF[[i]] <- '1'} + if(AF[[i]] > AVEAFBC){ dAFBC[[i]] <- '0' } else {dAFBC[[i]] <- '1'} + if(BC[[i]] > AVEAFBC){ dBCAF[[i]] <- '0' } else {dBCAF[[i]] <- '1'} + if(AF[[i]] > AVEAFBD){ dAFBD[[i]] <- '0' } else {dAFBD[[i]] <- '1'} + if(BD[[i]] > AVEAFBD){ dBDAF[[i]] <- '0' } else {dBDAF[[i]] <- '1'} + if(AF[[i]] > AVEAFBE){ dAFBE[[i]] <- '0' } else {dAFBE[[i]] <- '1'} + if(BE[[i]] > AVEAFBE){ dBEAF[[i]] <- '0' } else {dBEAF[[i]] <- '1'} + if(AF[[i]] > AVEAFBF){ dAFBF[[i]] <- '0' } else {dAFBF[[i]] <- '1'} + if(BF[[i]] > AVEAFBF){ dBFAF[[i]] <- '0' } else {dBFAF[[i]] <- '1'} + if(AF[[i]] > AVEAFBG){ dAFBG[[i]] <- '0' } else {dAFBG[[i]] <- '1'} + if(BG[[i]] > AVEAFBG){ dBGAF[[i]] <- '0' } else {dBGAF[[i]] <- '1'} + if(AF[[i]] > AVEAFBH){ dAFBH[[i]] <- '0' } else {dAFBH[[i]] <- '1'} + if(BH[[i]] > AVEAFBH){ dBHAF[[i]] <- '0' } else {dBHAF[[i]] <- '1'} + if(AF[[i]] > AVEAFCA){ dAFCA[[i]] <- '0' } else {dAFCA[[i]] <- '1'} + if(CA[[i]] > AVEAFCA){ dCAAF[[i]] <- '0' } else {dCAAF[[i]] <- '1'} + if(AF[[i]] > AVEAFCB){ dAFCB[[i]] <- '0' } else {dAFCB[[i]] <- '1'} + if(CB[[i]] > AVEAFCB){ dCBAF[[i]] <- '0' } else {dCBAF[[i]] <- '1'} + if(AF[[i]] > AVEAFCC){ dAFCC[[i]] <- '0' } else {dAFCC[[i]] <- '1'} + if(CC[[i]] > AVEAFCC){ dCCAF[[i]] <- '0' } else {dCCAF[[i]] <- '1'} + if(AF[[i]] > AVEAFCD){ dAFCD[[i]] <- '0' } else {dAFCD[[i]] <- '1'} + if(CD[[i]] > AVEAFCD){ dCDAF[[i]] <- '0' } else {dCDAF[[i]] <- '1'} + if(AF[[i]] > AVEAFCE){ dAFCE[[i]] <- '0' } else {dAFCE[[i]] <- '1'} + if(CE[[i]] > AVEAFCE){ dCEAF[[i]] <- '0' } else {dCEAF[[i]] <- '1'} + if(AF[[i]] > AVEAFCF){ dAFCF[[i]] <- '0' } else {dAFCF[[i]] <- '1'} + if(CF[[i]] > AVEAFCF){ dCFAF[[i]] <- '0' } else {dCFAF[[i]] <- '1'} + if(AF[[i]] > AVEAFCG){ dAFCG[[i]] <- '0' } else {dAFCG[[i]] <- '1'} + if(CG[[i]] > AVEAFCG){ dCGAF[[i]] <- '0' } else {dCGAF[[i]] <- '1'} + if(AF[[i]] > AVEAFCH){ dAFCH[[i]] <- '0' } else {dAFCH[[i]] <- '1'} + if(CH[[i]] > AVEAFCH){ dCHAF[[i]] <- '0' } else {dCHAF[[i]] <- '1'} + if(AG[[i]] > AVEAGAA){ dAGAA[[i]] <- '0' } else {dAGAA[[i]] <- '1'} + if(AA[[i]] > AVEAGAA){ dAAAG[[i]] <- '0' } else {dAAAG[[i]] <- '1'} + if(AG[[i]] > AVEAGAB){ dAGAB[[i]] <- '0' } else {dAGAB[[i]] <- '1'} + if(AB[[i]] > AVEAGAB){ dABAG[[i]] <- '0' } else {dABAG[[i]] <- '1'} + if(AG[[i]] > AVEAGAC){ dAGAC[[i]] <- '0' } else {dAGAC[[i]] <- '1'} + if(AC[[i]] > AVEAGAC){ dACAG[[i]] <- '0' } else {dACAG[[i]] <- '1'} + if(AG[[i]] > AVEAGAD){ dAGAD[[i]] <- '0' } else {dAGAD[[i]] <- '1'} + if(AD[[i]] > AVEAGAD){ dADAG[[i]] <- '0' } else {dADAG[[i]] <- '1'} + if(AG[[i]] > AVEAGAE){ dAGAE[[i]] <- '0' } else {dAGAE[[i]] <- '1'} + if(AE[[i]] > AVEAGAE){ dAEAG[[i]] <- '0' } else {dAEAG[[i]] <- '1'} + if(AG[[i]] > AVEAGAF){ dAGAF[[i]] <- '0' } else {dAGAF[[i]] <- '1'} + if(AF[[i]] > AVEAGAF){ dAFAG[[i]] <- '0' } else {dAFAG[[i]] <- '1'} + if(AG[[i]] > AVEAGAG){ dAGAG[[i]] <- '0' } else {dAGAG[[i]] <- '1'} + if(AG[[i]] > AVEAGAG){ dAGAG[[i]] <- '0' } else {dAGAG[[i]] <- '1'} + if(AG[[i]] > AVEAGAH){ dAGAH[[i]] <- '0' } else {dAGAH[[i]] <- '1'} + if(AH[[i]] > AVEAGAH){ dAHAG[[i]] <- '0' } else {dAHAG[[i]] <- '1'} + if(AG[[i]] > AVEAGBA){ dAGBA[[i]] <- '0' } else {dAGBA[[i]] <- '1'} + if(BA[[i]] > AVEAGBA){ dBAAG[[i]] <- '0' } else {dBAAG[[i]] <- '1'} + if(AG[[i]] > AVEAGBB){ dAGBB[[i]] <- '0' } else {dAGBB[[i]] <- '1'} + if(BB[[i]] > AVEAGBB){ dBBAG[[i]] <- '0' } else {dBBAG[[i]] <- '1'} + if(AG[[i]] > AVEAGBC){ dAGBC[[i]] <- '0' } else {dAGBC[[i]] <- '1'} + if(BC[[i]] > AVEAGBC){ dBCAG[[i]] <- '0' } else {dBCAG[[i]] <- '1'} + if(AG[[i]] > AVEAGBD){ dAGBD[[i]] <- '0' } else {dAGBD[[i]] <- '1'} + if(BD[[i]] > AVEAGBD){ dBDAG[[i]] <- '0' } else {dBDAG[[i]] <- '1'} + if(AG[[i]] > AVEAGBE){ dAGBE[[i]] <- '0' } else {dAGBE[[i]] <- '1'} + if(BE[[i]] > AVEAGBE){ dBEAG[[i]] <- '0' } else {dBEAG[[i]] <- '1'} + if(AG[[i]] > AVEAGBF){ dAGBF[[i]] <- '0' } else {dAGBF[[i]] <- '1'} + if(BF[[i]] > AVEAGBF){ dBFAG[[i]] <- '0' } else {dBFAG[[i]] <- '1'} + if(AG[[i]] > AVEAGBG){ dAGBG[[i]] <- '0' } else {dAGBG[[i]] <- '1'} + if(BG[[i]] > AVEAGBG){ dBGAG[[i]] <- '0' } else {dBGAG[[i]] <- '1'} + if(AG[[i]] > AVEAGBH){ dAGBH[[i]] <- '0' } else {dAGBH[[i]] <- '1'} + if(BH[[i]] > AVEAGBH){ dBHAG[[i]] <- '0' } else {dBHAG[[i]] <- '1'} + if(AG[[i]] > AVEAGCA){ dAGCA[[i]] <- '0' } else {dAGCA[[i]] <- '1'} + if(CA[[i]] > AVEAGCA){ dCAAG[[i]] <- '0' } else {dCAAG[[i]] <- '1'} + if(AG[[i]] > AVEAGCB){ dAGCB[[i]] <- '0' } else {dAGCB[[i]] <- '1'} + if(CB[[i]] > AVEAGCB){ dCBAG[[i]] <- '0' } else {dCBAG[[i]] <- '1'} + if(AG[[i]] > AVEAGCC){ dAGCC[[i]] <- '0' } else {dAGCC[[i]] <- '1'} + if(CC[[i]] > AVEAGCC){ dCCAG[[i]] <- '0' } else {dCCAG[[i]] <- '1'} + if(AG[[i]] > AVEAGCD){ dAGCD[[i]] <- '0' } else {dAGCD[[i]] <- '1'} + if(CD[[i]] > AVEAGCD){ dCDAG[[i]] <- '0' } else {dCDAG[[i]] <- '1'} + if(AG[[i]] > AVEAGCE){ dAGCE[[i]] <- '0' } else {dAGCE[[i]] <- '1'} + if(CE[[i]] > AVEAGCE){ dCEAG[[i]] <- '0' } else {dCEAG[[i]] <- '1'} + if(AG[[i]] > AVEAGCF){ dAGCF[[i]] <- '0' } else {dAGCF[[i]] <- '1'} + if(CF[[i]] > AVEAGCF){ dCFAG[[i]] <- '0' } else {dCFAG[[i]] <- '1'} + if(AG[[i]] > AVEAGCG){ dAGCG[[i]] <- '0' } else {dAGCG[[i]] <- '1'} + if(CG[[i]] > AVEAGCG){ dCGAG[[i]] <- '0' } else {dCGAG[[i]] <- '1'} + if(AG[[i]] > AVEAGCH){ dAGCH[[i]] <- '0' } else {dAGCH[[i]] <- '1'} + if(CH[[i]] > AVEAGCH){ dCHAG[[i]] <- '0' } else {dCHAG[[i]] <- '1'} + if(AH[[i]] > AVEAHAA){ dAHAA[[i]] <- '0' } else {dAHAA[[i]] <- '1'} + if(AA[[i]] > AVEAHAA){ dAAAH[[i]] <- '0' } else {dAAAH[[i]] <- '1'} + if(AH[[i]] > AVEAHAB){ dAHAB[[i]] <- '0' } else {dAHAB[[i]] <- '1'} + if(AB[[i]] > AVEAHAB){ dABAH[[i]] <- '0' } else {dABAH[[i]] <- '1'} + if(AH[[i]] > AVEAHAC){ dAHAC[[i]] <- '0' } else {dAHAC[[i]] <- '1'} + if(AC[[i]] > AVEAHAC){ dACAH[[i]] <- '0' } else {dACAH[[i]] <- '1'} + if(AH[[i]] > AVEAHAD){ dAHAD[[i]] <- '0' } else {dAHAD[[i]] <- '1'} + if(AD[[i]] > AVEAHAD){ dADAH[[i]] <- '0' } else {dADAH[[i]] <- '1'} + if(AH[[i]] > AVEAHAE){ dAHAE[[i]] <- '0' } else {dAHAE[[i]] <- '1'} + if(AE[[i]] > AVEAHAE){ dAEAH[[i]] <- '0' } else {dAEAH[[i]] <- '1'} + if(AH[[i]] > AVEAHAF){ dAHAF[[i]] <- '0' } else {dAHAF[[i]] <- '1'} + if(AF[[i]] > AVEAHAF){ dAFAH[[i]] <- '0' } else {dAFAH[[i]] <- '1'} + if(AH[[i]] > AVEAHAG){ dAHAG[[i]] <- '0' } else {dAHAG[[i]] <- '1'} + if(AG[[i]] > AVEAHAG){ dAGAH[[i]] <- '0' } else {dAGAH[[i]] <- '1'} + if(AH[[i]] > AVEAHAH){ dAHAH[[i]] <- '0' } else {dAHAH[[i]] <- '1'} + if(AH[[i]] > AVEAHAH){ dAHAH[[i]] <- '0' } else {dAHAH[[i]] <- '1'} + if(AH[[i]] > AVEAHBA){ dAHBA[[i]] <- '0' } else {dAHBA[[i]] <- '1'} + if(BA[[i]] > AVEAHBA){ dBAAH[[i]] <- '0' } else {dBAAH[[i]] <- '1'} + if(AH[[i]] > AVEAHBB){ dAHBB[[i]] <- '0' } else {dAHBB[[i]] <- '1'} + if(BB[[i]] > AVEAHBB){ dBBAH[[i]] <- '0' } else {dBBAH[[i]] <- '1'} + if(AH[[i]] > AVEAHBC){ dAHBC[[i]] <- '0' } else {dAHBC[[i]] <- '1'} + if(BC[[i]] > AVEAHBC){ dBCAH[[i]] <- '0' } else {dBCAH[[i]] <- '1'} + if(AH[[i]] > AVEAHBD){ dAHBD[[i]] <- '0' } else {dAHBD[[i]] <- '1'} + if(BD[[i]] > AVEAHBD){ dBDAH[[i]] <- '0' } else {dBDAH[[i]] <- '1'} + if(AH[[i]] > AVEAHBE){ dAHBE[[i]] <- '0' } else {dAHBE[[i]] <- '1'} + if(BE[[i]] > AVEAHBE){ dBEAH[[i]] <- '0' } else {dBEAH[[i]] <- '1'} + if(AH[[i]] > AVEAHBF){ dAHBF[[i]] <- '0' } else {dAHBF[[i]] <- '1'} + if(BF[[i]] > AVEAHBF){ dBFAH[[i]] <- '0' } else {dBFAH[[i]] <- '1'} + if(AH[[i]] > AVEAHBG){ dAHBG[[i]] <- '0' } else {dAHBG[[i]] <- '1'} + if(BG[[i]] > AVEAHBG){ dBGAH[[i]] <- '0' } else {dBGAH[[i]] <- '1'} + if(AH[[i]] > AVEAHBH){ dAHBH[[i]] <- '0' } else {dAHBH[[i]] <- '1'} + if(BH[[i]] > AVEAHBH){ dBHAH[[i]] <- '0' } else {dBHAH[[i]] <- '1'} + if(AH[[i]] > AVEAHCA){ dAHCA[[i]] <- '0' } else {dAHCA[[i]] <- '1'} + if(CA[[i]] > AVEAHCA){ dCAAH[[i]] <- '0' } else {dCAAH[[i]] <- '1'} + if(AH[[i]] > AVEAHCB){ dAHCB[[i]] <- '0' } else {dAHCB[[i]] <- '1'} + if(CB[[i]] > AVEAHCB){ dCBAH[[i]] <- '0' } else {dCBAH[[i]] <- '1'} + if(AH[[i]] > AVEAHCC){ dAHCC[[i]] <- '0' } else {dAHCC[[i]] <- '1'} + if(CC[[i]] > AVEAHCC){ dCCAH[[i]] <- '0' } else {dCCAH[[i]] <- '1'} + if(AH[[i]] > AVEAHCD){ dAHCD[[i]] <- '0' } else {dAHCD[[i]] <- '1'} + if(CD[[i]] > AVEAHCD){ dCDAH[[i]] <- '0' } else {dCDAH[[i]] <- '1'} + if(AH[[i]] > AVEAHCE){ dAHCE[[i]] <- '0' } else {dAHCE[[i]] <- '1'} + if(CE[[i]] > AVEAHCE){ dCEAH[[i]] <- '0' } else {dCEAH[[i]] <- '1'} + if(AH[[i]] > AVEAHCF){ dAHCF[[i]] <- '0' } else {dAHCF[[i]] <- '1'} + if(CF[[i]] > AVEAHCF){ dCFAH[[i]] <- '0' } else {dCFAH[[i]] <- '1'} + if(AH[[i]] > AVEAHCG){ dAHCG[[i]] <- '0' } else {dAHCG[[i]] <- '1'} + if(CG[[i]] > AVEAHCG){ dCGAH[[i]] <- '0' } else {dCGAH[[i]] <- '1'} + if(AH[[i]] > AVEAHCH){ dAHCH[[i]] <- '0' } else {dAHCH[[i]] <- '1'} + if(CH[[i]] > AVEAHCH){ dCHAH[[i]] <- '0' } else {dCHAH[[i]] <- '1'} + if(BA[[i]] > AVEBAAA){ dBAAA[[i]] <- '0' } else {dBAAA[[i]] <- '1'} + if(AA[[i]] > AVEBAAA){ dAABA[[i]] <- '0' } else {dAABA[[i]] <- '1'} + if(BA[[i]] > AVEBAAB){ dBAAB[[i]] <- '0' } else {dBAAB[[i]] <- '1'} + if(AB[[i]] > AVEBAAB){ dABBA[[i]] <- '0' } else {dABBA[[i]] <- '1'} + if(BA[[i]] > AVEBAAC){ dBAAC[[i]] <- '0' } else {dBAAC[[i]] <- '1'} + if(AC[[i]] > AVEBAAC){ dACBA[[i]] <- '0' } else {dACBA[[i]] <- '1'} + if(BA[[i]] > AVEBAAD){ dBAAD[[i]] <- '0' } else {dBAAD[[i]] <- '1'} + if(AD[[i]] > AVEBAAD){ dADBA[[i]] <- '0' } else {dADBA[[i]] <- '1'} + if(BA[[i]] > AVEBAAE){ dBAAE[[i]] <- '0' } else {dBAAE[[i]] <- '1'} + if(AE[[i]] > AVEBAAE){ dAEBA[[i]] <- '0' } else {dAEBA[[i]] <- '1'} + if(BA[[i]] > AVEBAAF){ dBAAF[[i]] <- '0' } else {dBAAF[[i]] <- '1'} + if(AF[[i]] > AVEBAAF){ dAFBA[[i]] <- '0' } else {dAFBA[[i]] <- '1'} + if(BA[[i]] > AVEBAAG){ dBAAG[[i]] <- '0' } else {dBAAG[[i]] <- '1'} + if(AG[[i]] > AVEBAAG){ dAGBA[[i]] <- '0' } else {dAGBA[[i]] <- '1'} + if(BA[[i]] > AVEBAAH){ dBAAH[[i]] <- '0' } else {dBAAH[[i]] <- '1'} + if(AH[[i]] > AVEBAAH){ dAHBA[[i]] <- '0' } else {dAHBA[[i]] <- '1'} + if(BA[[i]] > AVEBABA){ dBABA[[i]] <- '0' } else {dBABA[[i]] <- '1'} + if(BA[[i]] > AVEBABA){ dBABA[[i]] <- '0' } else {dBABA[[i]] <- '1'} + if(BA[[i]] > AVEBABB){ dBABB[[i]] <- '0' } else {dBABB[[i]] <- '1'} + if(BB[[i]] > AVEBABB){ dBBBA[[i]] <- '0' } else {dBBBA[[i]] <- '1'} + if(BA[[i]] > AVEBABC){ dBABC[[i]] <- '0' } else {dBABC[[i]] <- '1'} + if(BC[[i]] > AVEBABC){ dBCBA[[i]] <- '0' } else {dBCBA[[i]] <- '1'} + if(BA[[i]] > AVEBABD){ dBABD[[i]] <- '0' } else {dBABD[[i]] <- '1'} + if(BD[[i]] > AVEBABD){ dBDBA[[i]] <- '0' } else {dBDBA[[i]] <- '1'} + if(BA[[i]] > AVEBABE){ dBABE[[i]] <- '0' } else {dBABE[[i]] <- '1'} + if(BE[[i]] > AVEBABE){ dBEBA[[i]] <- '0' } else {dBEBA[[i]] <- '1'} + if(BA[[i]] > AVEBABF){ dBABF[[i]] <- '0' } else {dBABF[[i]] <- '1'} + if(BF[[i]] > AVEBABF){ dBFBA[[i]] <- '0' } else {dBFBA[[i]] <- '1'} + if(BA[[i]] > AVEBABG){ dBABG[[i]] <- '0' } else {dBABG[[i]] <- '1'} + if(BG[[i]] > AVEBABG){ dBGBA[[i]] <- '0' } else {dBGBA[[i]] <- '1'} + if(BA[[i]] > AVEBABH){ dBABH[[i]] <- '0' } else {dBABH[[i]] <- '1'} + if(BH[[i]] > AVEBABH){ dBHBA[[i]] <- '0' } else {dBHBA[[i]] <- '1'} + if(BA[[i]] > AVEBACA){ dBACA[[i]] <- '0' } else {dBACA[[i]] <- '1'} + if(CA[[i]] > AVEBACA){ dCABA[[i]] <- '0' } else {dCABA[[i]] <- '1'} + if(BA[[i]] > AVEBACB){ dBACB[[i]] <- '0' } else {dBACB[[i]] <- '1'} + if(CB[[i]] > AVEBACB){ dCBBA[[i]] <- '0' } else {dCBBA[[i]] <- '1'} + if(BA[[i]] > AVEBACC){ dBACC[[i]] <- '0' } else {dBACC[[i]] <- '1'} + if(CC[[i]] > AVEBACC){ dCCBA[[i]] <- '0' } else {dCCBA[[i]] <- '1'} + if(BA[[i]] > AVEBACD){ dBACD[[i]] <- '0' } else {dBACD[[i]] <- '1'} + if(CD[[i]] > AVEBACD){ dCDBA[[i]] <- '0' } else {dCDBA[[i]] <- '1'} + if(BA[[i]] > AVEBACE){ dBACE[[i]] <- '0' } else {dBACE[[i]] <- '1'} + if(CE[[i]] > AVEBACE){ dCEBA[[i]] <- '0' } else {dCEBA[[i]] <- '1'} + if(BA[[i]] > AVEBACF){ dBACF[[i]] <- '0' } else {dBACF[[i]] <- '1'} + if(CF[[i]] > AVEBACF){ dCFBA[[i]] <- '0' } else {dCFBA[[i]] <- '1'} + if(BA[[i]] > AVEBACG){ dBACG[[i]] <- '0' } else {dBACG[[i]] <- '1'} + if(CG[[i]] > AVEBACG){ dCGBA[[i]] <- '0' } else {dCGBA[[i]] <- '1'} + if(BA[[i]] > AVEBACH){ dBACH[[i]] <- '0' } else {dBACH[[i]] <- '1'} + if(CH[[i]] > AVEBACH){ dCHBA[[i]] <- '0' } else {dCHBA[[i]] <- '1'} + if(BB[[i]] > AVEBBAA){ dBBAA[[i]] <- '0' } else {dBBAA[[i]] <- '1'} + if(AA[[i]] > AVEBBAA){ dAABB[[i]] <- '0' } else {dAABB[[i]] <- '1'} + if(BB[[i]] > AVEBBAB){ dBBAB[[i]] <- '0' } else {dBBAB[[i]] <- '1'} + if(AB[[i]] > AVEBBAB){ dABBB[[i]] <- '0' } else {dABBB[[i]] <- '1'} + if(BB[[i]] > AVEBBAC){ dBBAC[[i]] <- '0' } else {dBBAC[[i]] <- '1'} + if(AC[[i]] > AVEBBAC){ dACBB[[i]] <- '0' } else {dACBB[[i]] <- '1'} + if(BB[[i]] > AVEBBAD){ dBBAD[[i]] <- '0' } else {dBBAD[[i]] <- '1'} + if(AD[[i]] > AVEBBAD){ dADBB[[i]] <- '0' } else {dADBB[[i]] <- '1'} + if(BB[[i]] > AVEBBAE){ dBBAE[[i]] <- '0' } else {dBBAE[[i]] <- '1'} + if(AE[[i]] > AVEBBAE){ dAEBB[[i]] <- '0' } else {dAEBB[[i]] <- '1'} + if(BB[[i]] > AVEBBAF){ dBBAF[[i]] <- '0' } else {dBBAF[[i]] <- '1'} + if(AF[[i]] > AVEBBAF){ dAFBB[[i]] <- '0' } else {dAFBB[[i]] <- '1'} + if(BB[[i]] > AVEBBAG){ dBBAG[[i]] <- '0' } else {dBBAG[[i]] <- '1'} + if(AG[[i]] > AVEBBAG){ dAGBB[[i]] <- '0' } else {dAGBB[[i]] <- '1'} + if(BB[[i]] > AVEBBAH){ dBBAH[[i]] <- '0' } else {dBBAH[[i]] <- '1'} + if(AH[[i]] > AVEBBAH){ dAHBB[[i]] <- '0' } else {dAHBB[[i]] <- '1'} + if(BB[[i]] > AVEBBBA){ dBBBA[[i]] <- '0' } else {dBBBA[[i]] <- '1'} + if(BA[[i]] > AVEBBBA){ dBABB[[i]] <- '0' } else {dBABB[[i]] <- '1'} + if(BB[[i]] > AVEBBBB){ dBBBB[[i]] <- '0' } else {dBBBB[[i]] <- '1'} + if(BB[[i]] > AVEBBBB){ dBBBB[[i]] <- '0' } else {dBBBB[[i]] <- '1'} + if(BB[[i]] > AVEBBBC){ dBBBC[[i]] <- '0' } else {dBBBC[[i]] <- '1'} + if(BC[[i]] > AVEBBBC){ dBCBB[[i]] <- '0' } else {dBCBB[[i]] <- '1'} + if(BB[[i]] > AVEBBBD){ dBBBD[[i]] <- '0' } else {dBBBD[[i]] <- '1'} + if(BD[[i]] > AVEBBBD){ dBDBB[[i]] <- '0' } else {dBDBB[[i]] <- '1'} + if(BB[[i]] > AVEBBBE){ dBBBE[[i]] <- '0' } else {dBBBE[[i]] <- '1'} + if(BE[[i]] > AVEBBBE){ dBEBB[[i]] <- '0' } else {dBEBB[[i]] <- '1'} + if(BB[[i]] > AVEBBBF){ dBBBF[[i]] <- '0' } else {dBBBF[[i]] <- '1'} + if(BF[[i]] > AVEBBBF){ dBFBB[[i]] <- '0' } else {dBFBB[[i]] <- '1'} + if(BB[[i]] > AVEBBBG){ dBBBG[[i]] <- '0' } else {dBBBG[[i]] <- '1'} + if(BG[[i]] > AVEBBBG){ dBGBB[[i]] <- '0' } else {dBGBB[[i]] <- '1'} + if(BB[[i]] > AVEBBBH){ dBBBH[[i]] <- '0' } else {dBBBH[[i]] <- '1'} + if(BH[[i]] > AVEBBBH){ dBHBB[[i]] <- '0' } else {dBHBB[[i]] <- '1'} + if(BB[[i]] > AVEBBCA){ dBBCA[[i]] <- '0' } else {dBBCA[[i]] <- '1'} + if(CA[[i]] > AVEBBCA){ dCABB[[i]] <- '0' } else {dCABB[[i]] <- '1'} + if(BB[[i]] > AVEBBCB){ dBBCB[[i]] <- '0' } else {dBBCB[[i]] <- '1'} + if(CB[[i]] > AVEBBCB){ dCBBB[[i]] <- '0' } else {dCBBB[[i]] <- '1'} + if(BB[[i]] > AVEBBCC){ dBBCC[[i]] <- '0' } else {dBBCC[[i]] <- '1'} + if(CC[[i]] > AVEBBCC){ dCCBB[[i]] <- '0' } else {dCCBB[[i]] <- '1'} + if(BB[[i]] > AVEBBCD){ dBBCD[[i]] <- '0' } else {dBBCD[[i]] <- '1'} + if(CD[[i]] > AVEBBCD){ dCDBB[[i]] <- '0' } else {dCDBB[[i]] <- '1'} + if(BB[[i]] > AVEBBCE){ dBBCE[[i]] <- '0' } else {dBBCE[[i]] <- '1'} + if(CE[[i]] > AVEBBCE){ dCEBB[[i]] <- '0' } else {dCEBB[[i]] <- '1'} + if(BB[[i]] > AVEBBCF){ dBBCF[[i]] <- '0' } else {dBBCF[[i]] <- '1'} + if(CF[[i]] > AVEBBCF){ dCFBB[[i]] <- '0' } else {dCFBB[[i]] <- '1'} + if(BB[[i]] > AVEBBCG){ dBBCG[[i]] <- '0' } else {dBBCG[[i]] <- '1'} + if(CG[[i]] > AVEBBCG){ dCGBB[[i]] <- '0' } else {dCGBB[[i]] <- '1'} + if(BB[[i]] > AVEBBCH){ dBBCH[[i]] <- '0' } else {dBBCH[[i]] <- '1'} + if(CH[[i]] > AVEBBCH){ dCHBB[[i]] <- '0' } else {dCHBB[[i]] <- '1'} + if(BC[[i]] > AVEBCAA){ dBCAA[[i]] <- '0' } else {dBCAA[[i]] <- '1'} + if(AA[[i]] > AVEBCAA){ dAABC[[i]] <- '0' } else {dAABC[[i]] <- '1'} + if(BC[[i]] > AVEBCAB){ dBCAB[[i]] <- '0' } else {dBCAB[[i]] <- '1'} + if(AB[[i]] > AVEBCAB){ dABBC[[i]] <- '0' } else {dABBC[[i]] <- '1'} + if(BC[[i]] > AVEBCAC){ dBCAC[[i]] <- '0' } else {dBCAC[[i]] <- '1'} + if(AC[[i]] > AVEBCAC){ dACBC[[i]] <- '0' } else {dACBC[[i]] <- '1'} + if(BC[[i]] > AVEBCAD){ dBCAD[[i]] <- '0' } else {dBCAD[[i]] <- '1'} + if(AD[[i]] > AVEBCAD){ dADBC[[i]] <- '0' } else {dADBC[[i]] <- '1'} + if(BC[[i]] > AVEBCAE){ dBCAE[[i]] <- '0' } else {dBCAE[[i]] <- '1'} + if(AE[[i]] > AVEBCAE){ dAEBC[[i]] <- '0' } else {dAEBC[[i]] <- '1'} + if(BC[[i]] > AVEBCAF){ dBCAF[[i]] <- '0' } else {dBCAF[[i]] <- '1'} + if(AF[[i]] > AVEBCAF){ dAFBC[[i]] <- '0' } else {dAFBC[[i]] <- '1'} + if(BC[[i]] > AVEBCAG){ dBCAG[[i]] <- '0' } else {dBCAG[[i]] <- '1'} + if(AG[[i]] > AVEBCAG){ dAGBC[[i]] <- '0' } else {dAGBC[[i]] <- '1'} + if(BC[[i]] > AVEBCAH){ dBCAH[[i]] <- '0' } else {dBCAH[[i]] <- '1'} + if(AH[[i]] > AVEBCAH){ dAHBC[[i]] <- '0' } else {dAHBC[[i]] <- '1'} + if(BC[[i]] > AVEBCBA){ dBCBA[[i]] <- '0' } else {dBCBA[[i]] <- '1'} + if(BA[[i]] > AVEBCBA){ dBABC[[i]] <- '0' } else {dBABC[[i]] <- '1'} + if(BC[[i]] > AVEBCBB){ dBCBB[[i]] <- '0' } else {dBCBB[[i]] <- '1'} + if(BB[[i]] > AVEBCBB){ dBBBC[[i]] <- '0' } else {dBBBC[[i]] <- '1'} + if(BC[[i]] > AVEBCBC){ dBCBC[[i]] <- '0' } else {dBCBC[[i]] <- '1'} + if(BC[[i]] > AVEBCBC){ dBCBC[[i]] <- '0' } else {dBCBC[[i]] <- '1'} + if(BC[[i]] > AVEBCBD){ dBCBD[[i]] <- '0' } else {dBCBD[[i]] <- '1'} + if(BD[[i]] > AVEBCBD){ dBDBC[[i]] <- '0' } else {dBDBC[[i]] <- '1'} + if(BC[[i]] > AVEBCBE){ dBCBE[[i]] <- '0' } else {dBCBE[[i]] <- '1'} + if(BE[[i]] > AVEBCBE){ dBEBC[[i]] <- '0' } else {dBEBC[[i]] <- '1'} + if(BC[[i]] > AVEBCBF){ dBCBF[[i]] <- '0' } else {dBCBF[[i]] <- '1'} + if(BF[[i]] > AVEBCBF){ dBFBC[[i]] <- '0' } else {dBFBC[[i]] <- '1'} + if(BC[[i]] > AVEBCBG){ dBCBG[[i]] <- '0' } else {dBCBG[[i]] <- '1'} + if(BG[[i]] > AVEBCBG){ dBGBC[[i]] <- '0' } else {dBGBC[[i]] <- '1'} + if(BC[[i]] > AVEBCBH){ dBCBH[[i]] <- '0' } else {dBCBH[[i]] <- '1'} + if(BH[[i]] > AVEBCBH){ dBHBC[[i]] <- '0' } else {dBHBC[[i]] <- '1'} + if(BC[[i]] > AVEBCCA){ dBCCA[[i]] <- '0' } else {dBCCA[[i]] <- '1'} + if(CA[[i]] > AVEBCCA){ dCABC[[i]] <- '0' } else {dCABC[[i]] <- '1'} + if(BC[[i]] > AVEBCCB){ dBCCB[[i]] <- '0' } else {dBCCB[[i]] <- '1'} + if(CB[[i]] > AVEBCCB){ dCBBC[[i]] <- '0' } else {dCBBC[[i]] <- '1'} + if(BC[[i]] > AVEBCCC){ dBCCC[[i]] <- '0' } else {dBCCC[[i]] <- '1'} + if(CC[[i]] > AVEBCCC){ dCCBC[[i]] <- '0' } else {dCCBC[[i]] <- '1'} + if(BC[[i]] > AVEBCCD){ dBCCD[[i]] <- '0' } else {dBCCD[[i]] <- '1'} + if(CD[[i]] > AVEBCCD){ dCDBC[[i]] <- '0' } else {dCDBC[[i]] <- '1'} + if(BC[[i]] > AVEBCCE){ dBCCE[[i]] <- '0' } else {dBCCE[[i]] <- '1'} + if(CE[[i]] > AVEBCCE){ dCEBC[[i]] <- '0' } else {dCEBC[[i]] <- '1'} + if(BC[[i]] > AVEBCCF){ dBCCF[[i]] <- '0' } else {dBCCF[[i]] <- '1'} + if(CF[[i]] > AVEBCCF){ dCFBC[[i]] <- '0' } else {dCFBC[[i]] <- '1'} + if(BC[[i]] > AVEBCCG){ dBCCG[[i]] <- '0' } else {dBCCG[[i]] <- '1'} + if(CG[[i]] > AVEBCCG){ dCGBC[[i]] <- '0' } else {dCGBC[[i]] <- '1'} + if(BC[[i]] > AVEBCCH){ dBCCH[[i]] <- '0' } else {dBCCH[[i]] <- '1'} + if(CH[[i]] > AVEBCCH){ dCHBC[[i]] <- '0' } else {dCHBC[[i]] <- '1'} + if(BD[[i]] > AVEBDAA){ dBDAA[[i]] <- '0' } else {dBDAA[[i]] <- '1'} + if(AA[[i]] > AVEBDAA){ dAABD[[i]] <- '0' } else {dAABD[[i]] <- '1'} + if(BD[[i]] > AVEBDAB){ dBDAB[[i]] <- '0' } else {dBDAB[[i]] <- '1'} + if(AB[[i]] > AVEBDAB){ dABBD[[i]] <- '0' } else {dABBD[[i]] <- '1'} + if(BD[[i]] > AVEBDAC){ dBDAC[[i]] <- '0' } else {dBDAC[[i]] <- '1'} + if(AC[[i]] > AVEBDAC){ dACBD[[i]] <- '0' } else {dACBD[[i]] <- '1'} + if(BD[[i]] > AVEBDAD){ dBDAD[[i]] <- '0' } else {dBDAD[[i]] <- '1'} + if(AD[[i]] > AVEBDAD){ dADBD[[i]] <- '0' } else {dADBD[[i]] <- '1'} + if(BD[[i]] > AVEBDAE){ dBDAE[[i]] <- '0' } else {dBDAE[[i]] <- '1'} + if(AE[[i]] > AVEBDAE){ dAEBD[[i]] <- '0' } else {dAEBD[[i]] <- '1'} + if(BD[[i]] > AVEBDAF){ dBDAF[[i]] <- '0' } else {dBDAF[[i]] <- '1'} + if(AF[[i]] > AVEBDAF){ dAFBD[[i]] <- '0' } else {dAFBD[[i]] <- '1'} + if(BD[[i]] > AVEBDAG){ dBDAG[[i]] <- '0' } else {dBDAG[[i]] <- '1'} + if(AG[[i]] > AVEBDAG){ dAGBD[[i]] <- '0' } else {dAGBD[[i]] <- '1'} + if(BD[[i]] > AVEBDAH){ dBDAH[[i]] <- '0' } else {dBDAH[[i]] <- '1'} + if(AH[[i]] > AVEBDAH){ dAHBD[[i]] <- '0' } else {dAHBD[[i]] <- '1'} + if(BD[[i]] > AVEBDBA){ dBDBA[[i]] <- '0' } else {dBDBA[[i]] <- '1'} + if(BA[[i]] > AVEBDBA){ dBABD[[i]] <- '0' } else {dBABD[[i]] <- '1'} + if(BD[[i]] > AVEBDBB){ dBDBB[[i]] <- '0' } else {dBDBB[[i]] <- '1'} + if(BB[[i]] > AVEBDBB){ dBBBD[[i]] <- '0' } else {dBBBD[[i]] <- '1'} + if(BD[[i]] > AVEBDBC){ dBDBC[[i]] <- '0' } else {dBDBC[[i]] <- '1'} + if(BC[[i]] > AVEBDBC){ dBCBD[[i]] <- '0' } else {dBCBD[[i]] <- '1'} + if(BD[[i]] > AVEBDBD){ dBDBD[[i]] <- '0' } else {dBDBD[[i]] <- '1'} + if(BD[[i]] > AVEBDBD){ dBDBD[[i]] <- '0' } else {dBDBD[[i]] <- '1'} + if(BD[[i]] > AVEBDBE){ dBDBE[[i]] <- '0' } else {dBDBE[[i]] <- '1'} + if(BE[[i]] > AVEBDBE){ dBEBD[[i]] <- '0' } else {dBEBD[[i]] <- '1'} + if(BD[[i]] > AVEBDBF){ dBDBF[[i]] <- '0' } else {dBDBF[[i]] <- '1'} + if(BF[[i]] > AVEBDBF){ dBFBD[[i]] <- '0' } else {dBFBD[[i]] <- '1'} + if(BD[[i]] > AVEBDBG){ dBDBG[[i]] <- '0' } else {dBDBG[[i]] <- '1'} + if(BG[[i]] > AVEBDBG){ dBGBD[[i]] <- '0' } else {dBGBD[[i]] <- '1'} + if(BD[[i]] > AVEBDBH){ dBDBH[[i]] <- '0' } else {dBDBH[[i]] <- '1'} + if(BH[[i]] > AVEBDBH){ dBHBD[[i]] <- '0' } else {dBHBD[[i]] <- '1'} + if(BD[[i]] > AVEBDCA){ dBDCA[[i]] <- '0' } else {dBDCA[[i]] <- '1'} + if(CA[[i]] > AVEBDCA){ dCABD[[i]] <- '0' } else {dCABD[[i]] <- '1'} + if(BD[[i]] > AVEBDCB){ dBDCB[[i]] <- '0' } else {dBDCB[[i]] <- '1'} + if(CB[[i]] > AVEBDCB){ dCBBD[[i]] <- '0' } else {dCBBD[[i]] <- '1'} + if(BD[[i]] > AVEBDCC){ dBDCC[[i]] <- '0' } else {dBDCC[[i]] <- '1'} + if(CC[[i]] > AVEBDCC){ dCCBD[[i]] <- '0' } else {dCCBD[[i]] <- '1'} + if(BD[[i]] > AVEBDCD){ dBDCD[[i]] <- '0' } else {dBDCD[[i]] <- '1'} + if(CD[[i]] > AVEBDCD){ dCDBD[[i]] <- '0' } else {dCDBD[[i]] <- '1'} + if(BD[[i]] > AVEBDCE){ dBDCE[[i]] <- '0' } else {dBDCE[[i]] <- '1'} + if(CE[[i]] > AVEBDCE){ dCEBD[[i]] <- '0' } else {dCEBD[[i]] <- '1'} + if(BD[[i]] > AVEBDCF){ dBDCF[[i]] <- '0' } else {dBDCF[[i]] <- '1'} + if(CF[[i]] > AVEBDCF){ dCFBD[[i]] <- '0' } else {dCFBD[[i]] <- '1'} + if(BD[[i]] > AVEBDCG){ dBDCG[[i]] <- '0' } else {dBDCG[[i]] <- '1'} + if(CG[[i]] > AVEBDCG){ dCGBD[[i]] <- '0' } else {dCGBD[[i]] <- '1'} + if(BD[[i]] > AVEBDCH){ dBDCH[[i]] <- '0' } else {dBDCH[[i]] <- '1'} + if(CH[[i]] > AVEBDCH){ dCHBD[[i]] <- '0' } else {dCHBD[[i]] <- '1'} + if(BE[[i]] > AVEBEAA){ dBEAA[[i]] <- '0' } else {dBEAA[[i]] <- '1'} + if(AA[[i]] > AVEBEAA){ dAABE[[i]] <- '0' } else {dAABE[[i]] <- '1'} + if(BE[[i]] > AVEBEAB){ dBEAB[[i]] <- '0' } else {dBEAB[[i]] <- '1'} + if(AB[[i]] > AVEBEAB){ dABBE[[i]] <- '0' } else {dABBE[[i]] <- '1'} + if(BE[[i]] > AVEBEAC){ dBEAC[[i]] <- '0' } else {dBEAC[[i]] <- '1'} + if(AC[[i]] > AVEBEAC){ dACBE[[i]] <- '0' } else {dACBE[[i]] <- '1'} + if(BE[[i]] > AVEBEAD){ dBEAD[[i]] <- '0' } else {dBEAD[[i]] <- '1'} + if(AD[[i]] > AVEBEAD){ dADBE[[i]] <- '0' } else {dADBE[[i]] <- '1'} + if(BE[[i]] > AVEBEAE){ dBEAE[[i]] <- '0' } else {dBEAE[[i]] <- '1'} + if(AE[[i]] > AVEBEAE){ dAEBE[[i]] <- '0' } else {dAEBE[[i]] <- '1'} + if(BE[[i]] > AVEBEAF){ dBEAF[[i]] <- '0' } else {dBEAF[[i]] <- '1'} + if(AF[[i]] > AVEBEAF){ dAFBE[[i]] <- '0' } else {dAFBE[[i]] <- '1'} + if(BE[[i]] > AVEBEAG){ dBEAG[[i]] <- '0' } else {dBEAG[[i]] <- '1'} + if(AG[[i]] > AVEBEAG){ dAGBE[[i]] <- '0' } else {dAGBE[[i]] <- '1'} + if(BE[[i]] > AVEBEAH){ dBEAH[[i]] <- '0' } else {dBEAH[[i]] <- '1'} + if(AH[[i]] > AVEBEAH){ dAHBE[[i]] <- '0' } else {dAHBE[[i]] <- '1'} + if(BE[[i]] > AVEBEBA){ dBEBA[[i]] <- '0' } else {dBEBA[[i]] <- '1'} + if(BA[[i]] > AVEBEBA){ dBABE[[i]] <- '0' } else {dBABE[[i]] <- '1'} + if(BE[[i]] > AVEBEBB){ dBEBB[[i]] <- '0' } else {dBEBB[[i]] <- '1'} + if(BB[[i]] > AVEBEBB){ dBBBE[[i]] <- '0' } else {dBBBE[[i]] <- '1'} + if(BE[[i]] > AVEBEBC){ dBEBC[[i]] <- '0' } else {dBEBC[[i]] <- '1'} + if(BC[[i]] > AVEBEBC){ dBCBE[[i]] <- '0' } else {dBCBE[[i]] <- '1'} + if(BE[[i]] > AVEBEBD){ dBEBD[[i]] <- '0' } else {dBEBD[[i]] <- '1'} + if(BD[[i]] > AVEBEBD){ dBDBE[[i]] <- '0' } else {dBDBE[[i]] <- '1'} + if(BE[[i]] > AVEBEBE){ dBEBE[[i]] <- '0' } else {dBEBE[[i]] <- '1'} + if(BE[[i]] > AVEBEBE){ dBEBE[[i]] <- '0' } else {dBEBE[[i]] <- '1'} + if(BE[[i]] > AVEBEBF){ dBEBF[[i]] <- '0' } else {dBEBF[[i]] <- '1'} + if(BF[[i]] > AVEBEBF){ dBFBE[[i]] <- '0' } else {dBFBE[[i]] <- '1'} + if(BE[[i]] > AVEBEBG){ dBEBG[[i]] <- '0' } else {dBEBG[[i]] <- '1'} + if(BG[[i]] > AVEBEBG){ dBGBE[[i]] <- '0' } else {dBGBE[[i]] <- '1'} + if(BE[[i]] > AVEBEBH){ dBEBH[[i]] <- '0' } else {dBEBH[[i]] <- '1'} + if(BH[[i]] > AVEBEBH){ dBHBE[[i]] <- '0' } else {dBHBE[[i]] <- '1'} + if(BE[[i]] > AVEBECA){ dBECA[[i]] <- '0' } else {dBECA[[i]] <- '1'} + if(CA[[i]] > AVEBECA){ dCABE[[i]] <- '0' } else {dCABE[[i]] <- '1'} + if(BE[[i]] > AVEBECB){ dBECB[[i]] <- '0' } else {dBECB[[i]] <- '1'} + if(CB[[i]] > AVEBECB){ dCBBE[[i]] <- '0' } else {dCBBE[[i]] <- '1'} + if(BE[[i]] > AVEBECC){ dBECC[[i]] <- '0' } else {dBECC[[i]] <- '1'} + if(CC[[i]] > AVEBECC){ dCCBE[[i]] <- '0' } else {dCCBE[[i]] <- '1'} + if(BE[[i]] > AVEBECD){ dBECD[[i]] <- '0' } else {dBECD[[i]] <- '1'} + if(CD[[i]] > AVEBECD){ dCDBE[[i]] <- '0' } else {dCDBE[[i]] <- '1'} + if(BE[[i]] > AVEBECE){ dBECE[[i]] <- '0' } else {dBECE[[i]] <- '1'} + if(CE[[i]] > AVEBECE){ dCEBE[[i]] <- '0' } else {dCEBE[[i]] <- '1'} + if(BE[[i]] > AVEBECF){ dBECF[[i]] <- '0' } else {dBECF[[i]] <- '1'} + if(CF[[i]] > AVEBECF){ dCFBE[[i]] <- '0' } else {dCFBE[[i]] <- '1'} + if(BE[[i]] > AVEBECG){ dBECG[[i]] <- '0' } else {dBECG[[i]] <- '1'} + if(CG[[i]] > AVEBECG){ dCGBE[[i]] <- '0' } else {dCGBE[[i]] <- '1'} + if(BE[[i]] > AVEBECH){ dBECH[[i]] <- '0' } else {dBECH[[i]] <- '1'} + if(CH[[i]] > AVEBECH){ dCHBE[[i]] <- '0' } else {dCHBE[[i]] <- '1'} + if(BF[[i]] > AVEBFAA){ dBFAA[[i]] <- '0' } else {dBFAA[[i]] <- '1'} + if(AA[[i]] > AVEBFAA){ dAABF[[i]] <- '0' } else {dAABF[[i]] <- '1'} + if(BF[[i]] > AVEBFAB){ dBFAB[[i]] <- '0' } else {dBFAB[[i]] <- '1'} + if(AB[[i]] > AVEBFAB){ dABBF[[i]] <- '0' } else {dABBF[[i]] <- '1'} + if(BF[[i]] > AVEBFAC){ dBFAC[[i]] <- '0' } else {dBFAC[[i]] <- '1'} + if(AC[[i]] > AVEBFAC){ dACBF[[i]] <- '0' } else {dACBF[[i]] <- '1'} + if(BF[[i]] > AVEBFAD){ dBFAD[[i]] <- '0' } else {dBFAD[[i]] <- '1'} + if(AD[[i]] > AVEBFAD){ dADBF[[i]] <- '0' } else {dADBF[[i]] <- '1'} + if(BF[[i]] > AVEBFAE){ dBFAE[[i]] <- '0' } else {dBFAE[[i]] <- '1'} + if(AE[[i]] > AVEBFAE){ dAEBF[[i]] <- '0' } else {dAEBF[[i]] <- '1'} + if(BF[[i]] > AVEBFAF){ dBFAF[[i]] <- '0' } else {dBFAF[[i]] <- '1'} + if(AF[[i]] > AVEBFAF){ dAFBF[[i]] <- '0' } else {dAFBF[[i]] <- '1'} + if(BF[[i]] > AVEBFAG){ dBFAG[[i]] <- '0' } else {dBFAG[[i]] <- '1'} + if(AG[[i]] > AVEBFAG){ dAGBF[[i]] <- '0' } else {dAGBF[[i]] <- '1'} + if(BF[[i]] > AVEBFAH){ dBFAH[[i]] <- '0' } else {dBFAH[[i]] <- '1'} + if(AH[[i]] > AVEBFAH){ dAHBF[[i]] <- '0' } else {dAHBF[[i]] <- '1'} + if(BF[[i]] > AVEBFBA){ dBFBA[[i]] <- '0' } else {dBFBA[[i]] <- '1'} + if(BA[[i]] > AVEBFBA){ dBABF[[i]] <- '0' } else {dBABF[[i]] <- '1'} + if(BF[[i]] > AVEBFBB){ dBFBB[[i]] <- '0' } else {dBFBB[[i]] <- '1'} + if(BB[[i]] > AVEBFBB){ dBBBF[[i]] <- '0' } else {dBBBF[[i]] <- '1'} + if(BF[[i]] > AVEBFBC){ dBFBC[[i]] <- '0' } else {dBFBC[[i]] <- '1'} + if(BC[[i]] > AVEBFBC){ dBCBF[[i]] <- '0' } else {dBCBF[[i]] <- '1'} + if(BF[[i]] > AVEBFBD){ dBFBD[[i]] <- '0' } else {dBFBD[[i]] <- '1'} + if(BD[[i]] > AVEBFBD){ dBDBF[[i]] <- '0' } else {dBDBF[[i]] <- '1'} + if(BF[[i]] > AVEBFBE){ dBFBE[[i]] <- '0' } else {dBFBE[[i]] <- '1'} + if(BE[[i]] > AVEBFBE){ dBEBF[[i]] <- '0' } else {dBEBF[[i]] <- '1'} + if(BF[[i]] > AVEBFBF){ dBFBF[[i]] <- '0' } else {dBFBF[[i]] <- '1'} + if(BF[[i]] > AVEBFBF){ dBFBF[[i]] <- '0' } else {dBFBF[[i]] <- '1'} + if(BF[[i]] > AVEBFBG){ dBFBG[[i]] <- '0' } else {dBFBG[[i]] <- '1'} + if(BG[[i]] > AVEBFBG){ dBGBF[[i]] <- '0' } else {dBGBF[[i]] <- '1'} + if(BF[[i]] > AVEBFBH){ dBFBH[[i]] <- '0' } else {dBFBH[[i]] <- '1'} + if(BH[[i]] > AVEBFBH){ dBHBF[[i]] <- '0' } else {dBHBF[[i]] <- '1'} + if(BF[[i]] > AVEBFCA){ dBFCA[[i]] <- '0' } else {dBFCA[[i]] <- '1'} + if(CA[[i]] > AVEBFCA){ dCABF[[i]] <- '0' } else {dCABF[[i]] <- '1'} + if(BF[[i]] > AVEBFCB){ dBFCB[[i]] <- '0' } else {dBFCB[[i]] <- '1'} + if(CB[[i]] > AVEBFCB){ dCBBF[[i]] <- '0' } else {dCBBF[[i]] <- '1'} + if(BF[[i]] > AVEBFCC){ dBFCC[[i]] <- '0' } else {dBFCC[[i]] <- '1'} + if(CC[[i]] > AVEBFCC){ dCCBF[[i]] <- '0' } else {dCCBF[[i]] <- '1'} + if(BF[[i]] > AVEBFCD){ dBFCD[[i]] <- '0' } else {dBFCD[[i]] <- '1'} + if(CD[[i]] > AVEBFCD){ dCDBF[[i]] <- '0' } else {dCDBF[[i]] <- '1'} + if(BF[[i]] > AVEBFCE){ dBFCE[[i]] <- '0' } else {dBFCE[[i]] <- '1'} + if(CE[[i]] > AVEBFCE){ dCEBF[[i]] <- '0' } else {dCEBF[[i]] <- '1'} + if(BF[[i]] > AVEBFCF){ dBFCF[[i]] <- '0' } else {dBFCF[[i]] <- '1'} + if(CF[[i]] > AVEBFCF){ dCFBF[[i]] <- '0' } else {dCFBF[[i]] <- '1'} + if(BF[[i]] > AVEBFCG){ dBFCG[[i]] <- '0' } else {dBFCG[[i]] <- '1'} + if(CG[[i]] > AVEBFCG){ dCGBF[[i]] <- '0' } else {dCGBF[[i]] <- '1'} + if(BF[[i]] > AVEBFCH){ dBFCH[[i]] <- '0' } else {dBFCH[[i]] <- '1'} + if(CH[[i]] > AVEBFCH){ dCHBF[[i]] <- '0' } else {dCHBF[[i]] <- '1'} + if(BG[[i]] > AVEBGAA){ dBGAA[[i]] <- '0' } else {dBGAA[[i]] <- '1'} + if(AA[[i]] > AVEBGAA){ dAABG[[i]] <- '0' } else {dAABG[[i]] <- '1'} + if(BG[[i]] > AVEBGAB){ dBGAB[[i]] <- '0' } else {dBGAB[[i]] <- '1'} + if(AB[[i]] > AVEBGAB){ dABBG[[i]] <- '0' } else {dABBG[[i]] <- '1'} + if(BG[[i]] > AVEBGAC){ dBGAC[[i]] <- '0' } else {dBGAC[[i]] <- '1'} + if(AC[[i]] > AVEBGAC){ dACBG[[i]] <- '0' } else {dACBG[[i]] <- '1'} + if(BG[[i]] > AVEBGAD){ dBGAD[[i]] <- '0' } else {dBGAD[[i]] <- '1'} + if(AD[[i]] > AVEBGAD){ dADBG[[i]] <- '0' } else {dADBG[[i]] <- '1'} + if(BG[[i]] > AVEBGAE){ dBGAE[[i]] <- '0' } else {dBGAE[[i]] <- '1'} + if(AE[[i]] > AVEBGAE){ dAEBG[[i]] <- '0' } else {dAEBG[[i]] <- '1'} + if(BG[[i]] > AVEBGAF){ dBGAF[[i]] <- '0' } else {dBGAF[[i]] <- '1'} + if(AF[[i]] > AVEBGAF){ dAFBG[[i]] <- '0' } else {dAFBG[[i]] <- '1'} + if(BG[[i]] > AVEBGAG){ dBGAG[[i]] <- '0' } else {dBGAG[[i]] <- '1'} + if(AG[[i]] > AVEBGAG){ dAGBG[[i]] <- '0' } else {dAGBG[[i]] <- '1'} + if(BG[[i]] > AVEBGAH){ dBGAH[[i]] <- '0' } else {dBGAH[[i]] <- '1'} + if(AH[[i]] > AVEBGAH){ dAHBG[[i]] <- '0' } else {dAHBG[[i]] <- '1'} + if(BG[[i]] > AVEBGBA){ dBGBA[[i]] <- '0' } else {dBGBA[[i]] <- '1'} + if(BA[[i]] > AVEBGBA){ dBABG[[i]] <- '0' } else {dBABG[[i]] <- '1'} + if(BG[[i]] > AVEBGBB){ dBGBB[[i]] <- '0' } else {dBGBB[[i]] <- '1'} + if(BB[[i]] > AVEBGBB){ dBBBG[[i]] <- '0' } else {dBBBG[[i]] <- '1'} + if(BG[[i]] > AVEBGBC){ dBGBC[[i]] <- '0' } else {dBGBC[[i]] <- '1'} + if(BC[[i]] > AVEBGBC){ dBCBG[[i]] <- '0' } else {dBCBG[[i]] <- '1'} + if(BG[[i]] > AVEBGBD){ dBGBD[[i]] <- '0' } else {dBGBD[[i]] <- '1'} + if(BD[[i]] > AVEBGBD){ dBDBG[[i]] <- '0' } else {dBDBG[[i]] <- '1'} + if(BG[[i]] > AVEBGBE){ dBGBE[[i]] <- '0' } else {dBGBE[[i]] <- '1'} + if(BE[[i]] > AVEBGBE){ dBEBG[[i]] <- '0' } else {dBEBG[[i]] <- '1'} + if(BG[[i]] > AVEBGBF){ dBGBF[[i]] <- '0' } else {dBGBF[[i]] <- '1'} + if(BF[[i]] > AVEBGBF){ dBFBG[[i]] <- '0' } else {dBFBG[[i]] <- '1'} + if(BG[[i]] > AVEBGBG){ dBGBG[[i]] <- '0' } else {dBGBG[[i]] <- '1'} + if(BG[[i]] > AVEBGBG){ dBGBG[[i]] <- '0' } else {dBGBG[[i]] <- '1'} + if(BG[[i]] > AVEBGBH){ dBGBH[[i]] <- '0' } else {dBGBH[[i]] <- '1'} + if(BH[[i]] > AVEBGBH){ dBHBG[[i]] <- '0' } else {dBHBG[[i]] <- '1'} + if(BG[[i]] > AVEBGCA){ dBGCA[[i]] <- '0' } else {dBGCA[[i]] <- '1'} + if(CA[[i]] > AVEBGCA){ dCABG[[i]] <- '0' } else {dCABG[[i]] <- '1'} + if(BG[[i]] > AVEBGCB){ dBGCB[[i]] <- '0' } else {dBGCB[[i]] <- '1'} + if(CB[[i]] > AVEBGCB){ dCBBG[[i]] <- '0' } else {dCBBG[[i]] <- '1'} + if(BG[[i]] > AVEBGCC){ dBGCC[[i]] <- '0' } else {dBGCC[[i]] <- '1'} + if(CC[[i]] > AVEBGCC){ dCCBG[[i]] <- '0' } else {dCCBG[[i]] <- '1'} + if(BG[[i]] > AVEBGCD){ dBGCD[[i]] <- '0' } else {dBGCD[[i]] <- '1'} + if(CD[[i]] > AVEBGCD){ dCDBG[[i]] <- '0' } else {dCDBG[[i]] <- '1'} + if(BG[[i]] > AVEBGCE){ dBGCE[[i]] <- '0' } else {dBGCE[[i]] <- '1'} + if(CE[[i]] > AVEBGCE){ dCEBG[[i]] <- '0' } else {dCEBG[[i]] <- '1'} + if(BG[[i]] > AVEBGCF){ dBGCF[[i]] <- '0' } else {dBGCF[[i]] <- '1'} + if(CF[[i]] > AVEBGCF){ dCFBG[[i]] <- '0' } else {dCFBG[[i]] <- '1'} + if(BG[[i]] > AVEBGCG){ dBGCG[[i]] <- '0' } else {dBGCG[[i]] <- '1'} + if(CG[[i]] > AVEBGCG){ dCGBG[[i]] <- '0' } else {dCGBG[[i]] <- '1'} + if(BG[[i]] > AVEBGCH){ dBGCH[[i]] <- '0' } else {dBGCH[[i]] <- '1'} + if(CH[[i]] > AVEBGCH){ dCHBG[[i]] <- '0' } else {dCHBG[[i]] <- '1'} + if(BH[[i]] > AVEBHAA){ dBHAA[[i]] <- '0' } else {dBHAA[[i]] <- '1'} + if(AA[[i]] > AVEBHAA){ dAABH[[i]] <- '0' } else {dAABH[[i]] <- '1'} + if(BH[[i]] > AVEBHAB){ dBHAB[[i]] <- '0' } else {dBHAB[[i]] <- '1'} + if(AB[[i]] > AVEBHAB){ dABBH[[i]] <- '0' } else {dABBH[[i]] <- '1'} + if(BH[[i]] > AVEBHAC){ dBHAC[[i]] <- '0' } else {dBHAC[[i]] <- '1'} + if(AC[[i]] > AVEBHAC){ dACBH[[i]] <- '0' } else {dACBH[[i]] <- '1'} + if(BH[[i]] > AVEBHAD){ dBHAD[[i]] <- '0' } else {dBHAD[[i]] <- '1'} + if(AD[[i]] > AVEBHAD){ dADBH[[i]] <- '0' } else {dADBH[[i]] <- '1'} + if(BH[[i]] > AVEBHAE){ dBHAE[[i]] <- '0' } else {dBHAE[[i]] <- '1'} + if(AE[[i]] > AVEBHAE){ dAEBH[[i]] <- '0' } else {dAEBH[[i]] <- '1'} + if(BH[[i]] > AVEBHAF){ dBHAF[[i]] <- '0' } else {dBHAF[[i]] <- '1'} + if(AF[[i]] > AVEBHAF){ dAFBH[[i]] <- '0' } else {dAFBH[[i]] <- '1'} + if(BH[[i]] > AVEBHAG){ dBHAG[[i]] <- '0' } else {dBHAG[[i]] <- '1'} + if(AG[[i]] > AVEBHAG){ dAGBH[[i]] <- '0' } else {dAGBH[[i]] <- '1'} + if(BH[[i]] > AVEBHAH){ dBHAH[[i]] <- '0' } else {dBHAH[[i]] <- '1'} + if(AH[[i]] > AVEBHAH){ dAHBH[[i]] <- '0' } else {dAHBH[[i]] <- '1'} + if(BH[[i]] > AVEBHBA){ dBHBA[[i]] <- '0' } else {dBHBA[[i]] <- '1'} + if(BA[[i]] > AVEBHBA){ dBABH[[i]] <- '0' } else {dBABH[[i]] <- '1'} + if(BH[[i]] > AVEBHBB){ dBHBB[[i]] <- '0' } else {dBHBB[[i]] <- '1'} + if(BB[[i]] > AVEBHBB){ dBBBH[[i]] <- '0' } else {dBBBH[[i]] <- '1'} + if(BH[[i]] > AVEBHBC){ dBHBC[[i]] <- '0' } else {dBHBC[[i]] <- '1'} + if(BC[[i]] > AVEBHBC){ dBCBH[[i]] <- '0' } else {dBCBH[[i]] <- '1'} + if(BH[[i]] > AVEBHBD){ dBHBD[[i]] <- '0' } else {dBHBD[[i]] <- '1'} + if(BD[[i]] > AVEBHBD){ dBDBH[[i]] <- '0' } else {dBDBH[[i]] <- '1'} + if(BH[[i]] > AVEBHBE){ dBHBE[[i]] <- '0' } else {dBHBE[[i]] <- '1'} + if(BE[[i]] > AVEBHBE){ dBEBH[[i]] <- '0' } else {dBEBH[[i]] <- '1'} + if(BH[[i]] > AVEBHBF){ dBHBF[[i]] <- '0' } else {dBHBF[[i]] <- '1'} + if(BF[[i]] > AVEBHBF){ dBFBH[[i]] <- '0' } else {dBFBH[[i]] <- '1'} + if(BH[[i]] > AVEBHBG){ dBHBG[[i]] <- '0' } else {dBHBG[[i]] <- '1'} + if(BG[[i]] > AVEBHBG){ dBGBH[[i]] <- '0' } else {dBGBH[[i]] <- '1'} + if(BH[[i]] > AVEBHBH){ dBHBH[[i]] <- '0' } else {dBHBH[[i]] <- '1'} + if(BH[[i]] > AVEBHBH){ dBHBH[[i]] <- '0' } else {dBHBH[[i]] <- '1'} + if(BH[[i]] > AVEBHCA){ dBHCA[[i]] <- '0' } else {dBHCA[[i]] <- '1'} + if(CA[[i]] > AVEBHCA){ dCABH[[i]] <- '0' } else {dCABH[[i]] <- '1'} + if(BH[[i]] > AVEBHCB){ dBHCB[[i]] <- '0' } else {dBHCB[[i]] <- '1'} + if(CB[[i]] > AVEBHCB){ dCBBH[[i]] <- '0' } else {dCBBH[[i]] <- '1'} + if(BH[[i]] > AVEBHCC){ dBHCC[[i]] <- '0' } else {dBHCC[[i]] <- '1'} + if(CC[[i]] > AVEBHCC){ dCCBH[[i]] <- '0' } else {dCCBH[[i]] <- '1'} + if(BH[[i]] > AVEBHCD){ dBHCD[[i]] <- '0' } else {dBHCD[[i]] <- '1'} + if(CD[[i]] > AVEBHCD){ dCDBH[[i]] <- '0' } else {dCDBH[[i]] <- '1'} + if(BH[[i]] > AVEBHCE){ dBHCE[[i]] <- '0' } else {dBHCE[[i]] <- '1'} + if(CE[[i]] > AVEBHCE){ dCEBH[[i]] <- '0' } else {dCEBH[[i]] <- '1'} + if(BH[[i]] > AVEBHCF){ dBHCF[[i]] <- '0' } else {dBHCF[[i]] <- '1'} + if(CF[[i]] > AVEBHCF){ dCFBH[[i]] <- '0' } else {dCFBH[[i]] <- '1'} + if(BH[[i]] > AVEBHCG){ dBHCG[[i]] <- '0' } else {dBHCG[[i]] <- '1'} + if(CG[[i]] > AVEBHCG){ dCGBH[[i]] <- '0' } else {dCGBH[[i]] <- '1'} + if(BH[[i]] > AVEBHCH){ dBHCH[[i]] <- '0' } else {dBHCH[[i]] <- '1'} + if(CH[[i]] > AVEBHCH){ dCHBH[[i]] <- '0' } else {dCHBH[[i]] <- '1'} + if(CA[[i]] > AVECAAA){ dCAAA[[i]] <- '0' } else {dCAAA[[i]] <- '1'} + if(AA[[i]] > AVECAAA){ dAACA[[i]] <- '0' } else {dAACA[[i]] <- '1'} + if(CA[[i]] > AVECAAB){ dCAAB[[i]] <- '0' } else {dCAAB[[i]] <- '1'} + if(AB[[i]] > AVECAAB){ dABCA[[i]] <- '0' } else {dABCA[[i]] <- '1'} + if(CA[[i]] > AVECAAC){ dCAAC[[i]] <- '0' } else {dCAAC[[i]] <- '1'} + if(AC[[i]] > AVECAAC){ dACCA[[i]] <- '0' } else {dACCA[[i]] <- '1'} + if(CA[[i]] > AVECAAD){ dCAAD[[i]] <- '0' } else {dCAAD[[i]] <- '1'} + if(AD[[i]] > AVECAAD){ dADCA[[i]] <- '0' } else {dADCA[[i]] <- '1'} + if(CA[[i]] > AVECAAE){ dCAAE[[i]] <- '0' } else {dCAAE[[i]] <- '1'} + if(AE[[i]] > AVECAAE){ dAECA[[i]] <- '0' } else {dAECA[[i]] <- '1'} + if(CA[[i]] > AVECAAF){ dCAAF[[i]] <- '0' } else {dCAAF[[i]] <- '1'} + if(AF[[i]] > AVECAAF){ dAFCA[[i]] <- '0' } else {dAFCA[[i]] <- '1'} + if(CA[[i]] > AVECAAG){ dCAAG[[i]] <- '0' } else {dCAAG[[i]] <- '1'} + if(AG[[i]] > AVECAAG){ dAGCA[[i]] <- '0' } else {dAGCA[[i]] <- '1'} + if(CA[[i]] > AVECAAH){ dCAAH[[i]] <- '0' } else {dCAAH[[i]] <- '1'} + if(AH[[i]] > AVECAAH){ dAHCA[[i]] <- '0' } else {dAHCA[[i]] <- '1'} + if(CA[[i]] > AVECABA){ dCABA[[i]] <- '0' } else {dCABA[[i]] <- '1'} + if(BA[[i]] > AVECABA){ dBACA[[i]] <- '0' } else {dBACA[[i]] <- '1'} + if(CA[[i]] > AVECABB){ dCABB[[i]] <- '0' } else {dCABB[[i]] <- '1'} + if(BB[[i]] > AVECABB){ dBBCA[[i]] <- '0' } else {dBBCA[[i]] <- '1'} + if(CA[[i]] > AVECABC){ dCABC[[i]] <- '0' } else {dCABC[[i]] <- '1'} + if(BC[[i]] > AVECABC){ dBCCA[[i]] <- '0' } else {dBCCA[[i]] <- '1'} + if(CA[[i]] > AVECABD){ dCABD[[i]] <- '0' } else {dCABD[[i]] <- '1'} + if(BD[[i]] > AVECABD){ dBDCA[[i]] <- '0' } else {dBDCA[[i]] <- '1'} + if(CA[[i]] > AVECABE){ dCABE[[i]] <- '0' } else {dCABE[[i]] <- '1'} + if(BE[[i]] > AVECABE){ dBECA[[i]] <- '0' } else {dBECA[[i]] <- '1'} + if(CA[[i]] > AVECABF){ dCABF[[i]] <- '0' } else {dCABF[[i]] <- '1'} + if(BF[[i]] > AVECABF){ dBFCA[[i]] <- '0' } else {dBFCA[[i]] <- '1'} + if(CA[[i]] > AVECABG){ dCABG[[i]] <- '0' } else {dCABG[[i]] <- '1'} + if(BG[[i]] > AVECABG){ dBGCA[[i]] <- '0' } else {dBGCA[[i]] <- '1'} + if(CA[[i]] > AVECABH){ dCABH[[i]] <- '0' } else {dCABH[[i]] <- '1'} + if(BH[[i]] > AVECABH){ dBHCA[[i]] <- '0' } else {dBHCA[[i]] <- '1'} + if(CA[[i]] > AVECACA){ dCACA[[i]] <- '0' } else {dCACA[[i]] <- '1'} + if(CA[[i]] > AVECACA){ dCACA[[i]] <- '0' } else {dCACA[[i]] <- '1'} + if(CA[[i]] > AVECACB){ dCACB[[i]] <- '0' } else {dCACB[[i]] <- '1'} + if(CB[[i]] > AVECACB){ dCBCA[[i]] <- '0' } else {dCBCA[[i]] <- '1'} + if(CA[[i]] > AVECACC){ dCACC[[i]] <- '0' } else {dCACC[[i]] <- '1'} + if(CC[[i]] > AVECACC){ dCCCA[[i]] <- '0' } else {dCCCA[[i]] <- '1'} + if(CA[[i]] > AVECACD){ dCACD[[i]] <- '0' } else {dCACD[[i]] <- '1'} + if(CD[[i]] > AVECACD){ dCDCA[[i]] <- '0' } else {dCDCA[[i]] <- '1'} + if(CA[[i]] > AVECACE){ dCACE[[i]] <- '0' } else {dCACE[[i]] <- '1'} + if(CE[[i]] > AVECACE){ dCECA[[i]] <- '0' } else {dCECA[[i]] <- '1'} + if(CA[[i]] > AVECACF){ dCACF[[i]] <- '0' } else {dCACF[[i]] <- '1'} + if(CF[[i]] > AVECACF){ dCFCA[[i]] <- '0' } else {dCFCA[[i]] <- '1'} + if(CA[[i]] > AVECACG){ dCACG[[i]] <- '0' } else {dCACG[[i]] <- '1'} + if(CG[[i]] > AVECACG){ dCGCA[[i]] <- '0' } else {dCGCA[[i]] <- '1'} + if(CA[[i]] > AVECACH){ dCACH[[i]] <- '0' } else {dCACH[[i]] <- '1'} + if(CH[[i]] > AVECACH){ dCHCA[[i]] <- '0' } else {dCHCA[[i]] <- '1'} + if(CB[[i]] > AVECBAA){ dCBAA[[i]] <- '0' } else {dCBAA[[i]] <- '1'} + if(AA[[i]] > AVECBAA){ dAACB[[i]] <- '0' } else {dAACB[[i]] <- '1'} + if(CB[[i]] > AVECBAB){ dCBAB[[i]] <- '0' } else {dCBAB[[i]] <- '1'} + if(AB[[i]] > AVECBAB){ dABCB[[i]] <- '0' } else {dABCB[[i]] <- '1'} + if(CB[[i]] > AVECBAC){ dCBAC[[i]] <- '0' } else {dCBAC[[i]] <- '1'} + if(AC[[i]] > AVECBAC){ dACCB[[i]] <- '0' } else {dACCB[[i]] <- '1'} + if(CB[[i]] > AVECBAD){ dCBAD[[i]] <- '0' } else {dCBAD[[i]] <- '1'} + if(AD[[i]] > AVECBAD){ dADCB[[i]] <- '0' } else {dADCB[[i]] <- '1'} + if(CB[[i]] > AVECBAE){ dCBAE[[i]] <- '0' } else {dCBAE[[i]] <- '1'} + if(AE[[i]] > AVECBAE){ dAECB[[i]] <- '0' } else {dAECB[[i]] <- '1'} + if(CB[[i]] > AVECBAF){ dCBAF[[i]] <- '0' } else {dCBAF[[i]] <- '1'} + if(AF[[i]] > AVECBAF){ dAFCB[[i]] <- '0' } else {dAFCB[[i]] <- '1'} + if(CB[[i]] > AVECBAG){ dCBAG[[i]] <- '0' } else {dCBAG[[i]] <- '1'} + if(AG[[i]] > AVECBAG){ dAGCB[[i]] <- '0' } else {dAGCB[[i]] <- '1'} + if(CB[[i]] > AVECBAH){ dCBAH[[i]] <- '0' } else {dCBAH[[i]] <- '1'} + if(AH[[i]] > AVECBAH){ dAHCB[[i]] <- '0' } else {dAHCB[[i]] <- '1'} + if(CB[[i]] > AVECBBA){ dCBBA[[i]] <- '0' } else {dCBBA[[i]] <- '1'} + if(BA[[i]] > AVECBBA){ dBACB[[i]] <- '0' } else {dBACB[[i]] <- '1'} + if(CB[[i]] > AVECBBB){ dCBBB[[i]] <- '0' } else {dCBBB[[i]] <- '1'} + if(BB[[i]] > AVECBBB){ dBBCB[[i]] <- '0' } else {dBBCB[[i]] <- '1'} + if(CB[[i]] > AVECBBC){ dCBBC[[i]] <- '0' } else {dCBBC[[i]] <- '1'} + if(BC[[i]] > AVECBBC){ dBCCB[[i]] <- '0' } else {dBCCB[[i]] <- '1'} + if(CB[[i]] > AVECBBD){ dCBBD[[i]] <- '0' } else {dCBBD[[i]] <- '1'} + if(BD[[i]] > AVECBBD){ dBDCB[[i]] <- '0' } else {dBDCB[[i]] <- '1'} + if(CB[[i]] > AVECBBE){ dCBBE[[i]] <- '0' } else {dCBBE[[i]] <- '1'} + if(BE[[i]] > AVECBBE){ dBECB[[i]] <- '0' } else {dBECB[[i]] <- '1'} + if(CB[[i]] > AVECBBF){ dCBBF[[i]] <- '0' } else {dCBBF[[i]] <- '1'} + if(BF[[i]] > AVECBBF){ dBFCB[[i]] <- '0' } else {dBFCB[[i]] <- '1'} + if(CB[[i]] > AVECBBG){ dCBBG[[i]] <- '0' } else {dCBBG[[i]] <- '1'} + if(BG[[i]] > AVECBBG){ dBGCB[[i]] <- '0' } else {dBGCB[[i]] <- '1'} + if(CB[[i]] > AVECBBH){ dCBBH[[i]] <- '0' } else {dCBBH[[i]] <- '1'} + if(BH[[i]] > AVECBBH){ dBHCB[[i]] <- '0' } else {dBHCB[[i]] <- '1'} + if(CB[[i]] > AVECBCA){ dCBCA[[i]] <- '0' } else {dCBCA[[i]] <- '1'} + if(CA[[i]] > AVECBCA){ dCACB[[i]] <- '0' } else {dCACB[[i]] <- '1'} + if(CB[[i]] > AVECBCB){ dCBCB[[i]] <- '0' } else {dCBCB[[i]] <- '1'} + if(CB[[i]] > AVECBCB){ dCBCB[[i]] <- '0' } else {dCBCB[[i]] <- '1'} + if(CB[[i]] > AVECBCC){ dCBCC[[i]] <- '0' } else {dCBCC[[i]] <- '1'} + if(CC[[i]] > AVECBCC){ dCCCB[[i]] <- '0' } else {dCCCB[[i]] <- '1'} + if(CB[[i]] > AVECBCD){ dCBCD[[i]] <- '0' } else {dCBCD[[i]] <- '1'} + if(CD[[i]] > AVECBCD){ dCDCB[[i]] <- '0' } else {dCDCB[[i]] <- '1'} + if(CB[[i]] > AVECBCE){ dCBCE[[i]] <- '0' } else {dCBCE[[i]] <- '1'} + if(CE[[i]] > AVECBCE){ dCECB[[i]] <- '0' } else {dCECB[[i]] <- '1'} + if(CB[[i]] > AVECBCF){ dCBCF[[i]] <- '0' } else {dCBCF[[i]] <- '1'} + if(CF[[i]] > AVECBCF){ dCFCB[[i]] <- '0' } else {dCFCB[[i]] <- '1'} + if(CB[[i]] > AVECBCG){ dCBCG[[i]] <- '0' } else {dCBCG[[i]] <- '1'} + if(CG[[i]] > AVECBCG){ dCGCB[[i]] <- '0' } else {dCGCB[[i]] <- '1'} + if(CB[[i]] > AVECBCH){ dCBCH[[i]] <- '0' } else {dCBCH[[i]] <- '1'} + if(CH[[i]] > AVECBCH){ dCHCB[[i]] <- '0' } else {dCHCB[[i]] <- '1'} + if(CC[[i]] > AVECCAA){ dCCAA[[i]] <- '0' } else {dCCAA[[i]] <- '1'} + if(AA[[i]] > AVECCAA){ dAACC[[i]] <- '0' } else {dAACC[[i]] <- '1'} + if(CC[[i]] > AVECCAB){ dCCAB[[i]] <- '0' } else {dCCAB[[i]] <- '1'} + if(AB[[i]] > AVECCAB){ dABCC[[i]] <- '0' } else {dABCC[[i]] <- '1'} + if(CC[[i]] > AVECCAC){ dCCAC[[i]] <- '0' } else {dCCAC[[i]] <- '1'} + if(AC[[i]] > AVECCAC){ dACCC[[i]] <- '0' } else {dACCC[[i]] <- '1'} + if(CC[[i]] > AVECCAD){ dCCAD[[i]] <- '0' } else {dCCAD[[i]] <- '1'} + if(AD[[i]] > AVECCAD){ dADCC[[i]] <- '0' } else {dADCC[[i]] <- '1'} + if(CC[[i]] > AVECCAE){ dCCAE[[i]] <- '0' } else {dCCAE[[i]] <- '1'} + if(AE[[i]] > AVECCAE){ dAECC[[i]] <- '0' } else {dAECC[[i]] <- '1'} + if(CC[[i]] > AVECCAF){ dCCAF[[i]] <- '0' } else {dCCAF[[i]] <- '1'} + if(AF[[i]] > AVECCAF){ dAFCC[[i]] <- '0' } else {dAFCC[[i]] <- '1'} + if(CC[[i]] > AVECCAG){ dCCAG[[i]] <- '0' } else {dCCAG[[i]] <- '1'} + if(AG[[i]] > AVECCAG){ dAGCC[[i]] <- '0' } else {dAGCC[[i]] <- '1'} + if(CC[[i]] > AVECCAH){ dCCAH[[i]] <- '0' } else {dCCAH[[i]] <- '1'} + if(AH[[i]] > AVECCAH){ dAHCC[[i]] <- '0' } else {dAHCC[[i]] <- '1'} + if(CC[[i]] > AVECCBA){ dCCBA[[i]] <- '0' } else {dCCBA[[i]] <- '1'} + if(BA[[i]] > AVECCBA){ dBACC[[i]] <- '0' } else {dBACC[[i]] <- '1'} + if(CC[[i]] > AVECCBB){ dCCBB[[i]] <- '0' } else {dCCBB[[i]] <- '1'} + if(BB[[i]] > AVECCBB){ dBBCC[[i]] <- '0' } else {dBBCC[[i]] <- '1'} + if(CC[[i]] > AVECCBC){ dCCBC[[i]] <- '0' } else {dCCBC[[i]] <- '1'} + if(BC[[i]] > AVECCBC){ dBCCC[[i]] <- '0' } else {dBCCC[[i]] <- '1'} + if(CC[[i]] > AVECCBD){ dCCBD[[i]] <- '0' } else {dCCBD[[i]] <- '1'} + if(BD[[i]] > AVECCBD){ dBDCC[[i]] <- '0' } else {dBDCC[[i]] <- '1'} + if(CC[[i]] > AVECCBE){ dCCBE[[i]] <- '0' } else {dCCBE[[i]] <- '1'} + if(BE[[i]] > AVECCBE){ dBECC[[i]] <- '0' } else {dBECC[[i]] <- '1'} + if(CC[[i]] > AVECCBF){ dCCBF[[i]] <- '0' } else {dCCBF[[i]] <- '1'} + if(BF[[i]] > AVECCBF){ dBFCC[[i]] <- '0' } else {dBFCC[[i]] <- '1'} + if(CC[[i]] > AVECCBG){ dCCBG[[i]] <- '0' } else {dCCBG[[i]] <- '1'} + if(BG[[i]] > AVECCBG){ dBGCC[[i]] <- '0' } else {dBGCC[[i]] <- '1'} + if(CC[[i]] > AVECCBH){ dCCBH[[i]] <- '0' } else {dCCBH[[i]] <- '1'} + if(BH[[i]] > AVECCBH){ dBHCC[[i]] <- '0' } else {dBHCC[[i]] <- '1'} + if(CC[[i]] > AVECCCA){ dCCCA[[i]] <- '0' } else {dCCCA[[i]] <- '1'} + if(CA[[i]] > AVECCCA){ dCACC[[i]] <- '0' } else {dCACC[[i]] <- '1'} + if(CC[[i]] > AVECCCB){ dCCCB[[i]] <- '0' } else {dCCCB[[i]] <- '1'} + if(CB[[i]] > AVECCCB){ dCBCC[[i]] <- '0' } else {dCBCC[[i]] <- '1'} + if(CC[[i]] > AVECCCC){ dCCCC[[i]] <- '0' } else {dCCCC[[i]] <- '1'} + if(CC[[i]] > AVECCCC){ dCCCC[[i]] <- '0' } else {dCCCC[[i]] <- '1'} + if(CC[[i]] > AVECCCD){ dCCCD[[i]] <- '0' } else {dCCCD[[i]] <- '1'} + if(CD[[i]] > AVECCCD){ dCDCC[[i]] <- '0' } else {dCDCC[[i]] <- '1'} + if(CC[[i]] > AVECCCE){ dCCCE[[i]] <- '0' } else {dCCCE[[i]] <- '1'} + if(CE[[i]] > AVECCCE){ dCECC[[i]] <- '0' } else {dCECC[[i]] <- '1'} + if(CC[[i]] > AVECCCF){ dCCCF[[i]] <- '0' } else {dCCCF[[i]] <- '1'} + if(CF[[i]] > AVECCCF){ dCFCC[[i]] <- '0' } else {dCFCC[[i]] <- '1'} + if(CC[[i]] > AVECCCG){ dCCCG[[i]] <- '0' } else {dCCCG[[i]] <- '1'} + if(CG[[i]] > AVECCCG){ dCGCC[[i]] <- '0' } else {dCGCC[[i]] <- '1'} + if(CC[[i]] > AVECCCH){ dCCCH[[i]] <- '0' } else {dCCCH[[i]] <- '1'} + if(CH[[i]] > AVECCCH){ dCHCC[[i]] <- '0' } else {dCHCC[[i]] <- '1'} + if(CD[[i]] > AVECDAA){ dCDAA[[i]] <- '0' } else {dCDAA[[i]] <- '1'} + if(AA[[i]] > AVECDAA){ dAACD[[i]] <- '0' } else {dAACD[[i]] <- '1'} + if(CD[[i]] > AVECDAB){ dCDAB[[i]] <- '0' } else {dCDAB[[i]] <- '1'} + if(AB[[i]] > AVECDAB){ dABCD[[i]] <- '0' } else {dABCD[[i]] <- '1'} + if(CD[[i]] > AVECDAC){ dCDAC[[i]] <- '0' } else {dCDAC[[i]] <- '1'} + if(AC[[i]] > AVECDAC){ dACCD[[i]] <- '0' } else {dACCD[[i]] <- '1'} + if(CD[[i]] > AVECDAD){ dCDAD[[i]] <- '0' } else {dCDAD[[i]] <- '1'} + if(AD[[i]] > AVECDAD){ dADCD[[i]] <- '0' } else {dADCD[[i]] <- '1'} + if(CD[[i]] > AVECDAE){ dCDAE[[i]] <- '0' } else {dCDAE[[i]] <- '1'} + if(AE[[i]] > AVECDAE){ dAECD[[i]] <- '0' } else {dAECD[[i]] <- '1'} + if(CD[[i]] > AVECDAF){ dCDAF[[i]] <- '0' } else {dCDAF[[i]] <- '1'} + if(AF[[i]] > AVECDAF){ dAFCD[[i]] <- '0' } else {dAFCD[[i]] <- '1'} + if(CD[[i]] > AVECDAG){ dCDAG[[i]] <- '0' } else {dCDAG[[i]] <- '1'} + if(AG[[i]] > AVECDAG){ dAGCD[[i]] <- '0' } else {dAGCD[[i]] <- '1'} + if(CD[[i]] > AVECDAH){ dCDAH[[i]] <- '0' } else {dCDAH[[i]] <- '1'} + if(AH[[i]] > AVECDAH){ dAHCD[[i]] <- '0' } else {dAHCD[[i]] <- '1'} + if(CD[[i]] > AVECDBA){ dCDBA[[i]] <- '0' } else {dCDBA[[i]] <- '1'} + if(BA[[i]] > AVECDBA){ dBACD[[i]] <- '0' } else {dBACD[[i]] <- '1'} + if(CD[[i]] > AVECDBB){ dCDBB[[i]] <- '0' } else {dCDBB[[i]] <- '1'} + if(BB[[i]] > AVECDBB){ dBBCD[[i]] <- '0' } else {dBBCD[[i]] <- '1'} + if(CD[[i]] > AVECDBC){ dCDBC[[i]] <- '0' } else {dCDBC[[i]] <- '1'} + if(BC[[i]] > AVECDBC){ dBCCD[[i]] <- '0' } else {dBCCD[[i]] <- '1'} + if(CD[[i]] > AVECDBD){ dCDBD[[i]] <- '0' } else {dCDBD[[i]] <- '1'} + if(BD[[i]] > AVECDBD){ dBDCD[[i]] <- '0' } else {dBDCD[[i]] <- '1'} + if(CD[[i]] > AVECDBE){ dCDBE[[i]] <- '0' } else {dCDBE[[i]] <- '1'} + if(BE[[i]] > AVECDBE){ dBECD[[i]] <- '0' } else {dBECD[[i]] <- '1'} + if(CD[[i]] > AVECDBF){ dCDBF[[i]] <- '0' } else {dCDBF[[i]] <- '1'} + if(BF[[i]] > AVECDBF){ dBFCD[[i]] <- '0' } else {dBFCD[[i]] <- '1'} + if(CD[[i]] > AVECDBG){ dCDBG[[i]] <- '0' } else {dCDBG[[i]] <- '1'} + if(BG[[i]] > AVECDBG){ dBGCD[[i]] <- '0' } else {dBGCD[[i]] <- '1'} + if(CD[[i]] > AVECDBH){ dCDBH[[i]] <- '0' } else {dCDBH[[i]] <- '1'} + if(BH[[i]] > AVECDBH){ dBHCD[[i]] <- '0' } else {dBHCD[[i]] <- '1'} + if(CD[[i]] > AVECDCA){ dCDCA[[i]] <- '0' } else {dCDCA[[i]] <- '1'} + if(CA[[i]] > AVECDCA){ dCACD[[i]] <- '0' } else {dCACD[[i]] <- '1'} + if(CD[[i]] > AVECDCB){ dCDCB[[i]] <- '0' } else {dCDCB[[i]] <- '1'} + if(CB[[i]] > AVECDCB){ dCBCD[[i]] <- '0' } else {dCBCD[[i]] <- '1'} + if(CD[[i]] > AVECDCC){ dCDCC[[i]] <- '0' } else {dCDCC[[i]] <- '1'} + if(CC[[i]] > AVECDCC){ dCCCD[[i]] <- '0' } else {dCCCD[[i]] <- '1'} + if(CD[[i]] > AVECDCD){ dCDCD[[i]] <- '0' } else {dCDCD[[i]] <- '1'} + if(CD[[i]] > AVECDCD){ dCDCD[[i]] <- '0' } else {dCDCD[[i]] <- '1'} + if(CD[[i]] > AVECDCE){ dCDCE[[i]] <- '0' } else {dCDCE[[i]] <- '1'} + if(CE[[i]] > AVECDCE){ dCECD[[i]] <- '0' } else {dCECD[[i]] <- '1'} + if(CD[[i]] > AVECDCF){ dCDCF[[i]] <- '0' } else {dCDCF[[i]] <- '1'} + if(CF[[i]] > AVECDCF){ dCFCD[[i]] <- '0' } else {dCFCD[[i]] <- '1'} + if(CD[[i]] > AVECDCG){ dCDCG[[i]] <- '0' } else {dCDCG[[i]] <- '1'} + if(CG[[i]] > AVECDCG){ dCGCD[[i]] <- '0' } else {dCGCD[[i]] <- '1'} + if(CD[[i]] > AVECDCH){ dCDCH[[i]] <- '0' } else {dCDCH[[i]] <- '1'} + if(CH[[i]] > AVECDCH){ dCHCD[[i]] <- '0' } else {dCHCD[[i]] <- '1'} + if(CE[[i]] > AVECEAA){ dCEAA[[i]] <- '0' } else {dCEAA[[i]] <- '1'} + if(AA[[i]] > AVECEAA){ dAACE[[i]] <- '0' } else {dAACE[[i]] <- '1'} + if(CE[[i]] > AVECEAB){ dCEAB[[i]] <- '0' } else {dCEAB[[i]] <- '1'} + if(AB[[i]] > AVECEAB){ dABCE[[i]] <- '0' } else {dABCE[[i]] <- '1'} + if(CE[[i]] > AVECEAC){ dCEAC[[i]] <- '0' } else {dCEAC[[i]] <- '1'} + if(AC[[i]] > AVECEAC){ dACCE[[i]] <- '0' } else {dACCE[[i]] <- '1'} + if(CE[[i]] > AVECEAD){ dCEAD[[i]] <- '0' } else {dCEAD[[i]] <- '1'} + if(AD[[i]] > AVECEAD){ dADCE[[i]] <- '0' } else {dADCE[[i]] <- '1'} + if(CE[[i]] > AVECEAE){ dCEAE[[i]] <- '0' } else {dCEAE[[i]] <- '1'} + if(AE[[i]] > AVECEAE){ dAECE[[i]] <- '0' } else {dAECE[[i]] <- '1'} + if(CE[[i]] > AVECEAF){ dCEAF[[i]] <- '0' } else {dCEAF[[i]] <- '1'} + if(AF[[i]] > AVECEAF){ dAFCE[[i]] <- '0' } else {dAFCE[[i]] <- '1'} + if(CE[[i]] > AVECEAG){ dCEAG[[i]] <- '0' } else {dCEAG[[i]] <- '1'} + if(AG[[i]] > AVECEAG){ dAGCE[[i]] <- '0' } else {dAGCE[[i]] <- '1'} + if(CE[[i]] > AVECEAH){ dCEAH[[i]] <- '0' } else {dCEAH[[i]] <- '1'} + if(AH[[i]] > AVECEAH){ dAHCE[[i]] <- '0' } else {dAHCE[[i]] <- '1'} + if(CE[[i]] > AVECEBA){ dCEBA[[i]] <- '0' } else {dCEBA[[i]] <- '1'} + if(BA[[i]] > AVECEBA){ dBACE[[i]] <- '0' } else {dBACE[[i]] <- '1'} + if(CE[[i]] > AVECEBB){ dCEBB[[i]] <- '0' } else {dCEBB[[i]] <- '1'} + if(BB[[i]] > AVECEBB){ dBBCE[[i]] <- '0' } else {dBBCE[[i]] <- '1'} + if(CE[[i]] > AVECEBC){ dCEBC[[i]] <- '0' } else {dCEBC[[i]] <- '1'} + if(BC[[i]] > AVECEBC){ dBCCE[[i]] <- '0' } else {dBCCE[[i]] <- '1'} + if(CE[[i]] > AVECEBD){ dCEBD[[i]] <- '0' } else {dCEBD[[i]] <- '1'} + if(BD[[i]] > AVECEBD){ dBDCE[[i]] <- '0' } else {dBDCE[[i]] <- '1'} + if(CE[[i]] > AVECEBE){ dCEBE[[i]] <- '0' } else {dCEBE[[i]] <- '1'} + if(BE[[i]] > AVECEBE){ dBECE[[i]] <- '0' } else {dBECE[[i]] <- '1'} + if(CE[[i]] > AVECEBF){ dCEBF[[i]] <- '0' } else {dCEBF[[i]] <- '1'} + if(BF[[i]] > AVECEBF){ dBFCE[[i]] <- '0' } else {dBFCE[[i]] <- '1'} + if(CE[[i]] > AVECEBG){ dCEBG[[i]] <- '0' } else {dCEBG[[i]] <- '1'} + if(BG[[i]] > AVECEBG){ dBGCE[[i]] <- '0' } else {dBGCE[[i]] <- '1'} + if(CE[[i]] > AVECEBH){ dCEBH[[i]] <- '0' } else {dCEBH[[i]] <- '1'} + if(BH[[i]] > AVECEBH){ dBHCE[[i]] <- '0' } else {dBHCE[[i]] <- '1'} + if(CE[[i]] > AVECECA){ dCECA[[i]] <- '0' } else {dCECA[[i]] <- '1'} + if(CA[[i]] > AVECECA){ dCACE[[i]] <- '0' } else {dCACE[[i]] <- '1'} + if(CE[[i]] > AVECECB){ dCECB[[i]] <- '0' } else {dCECB[[i]] <- '1'} + if(CB[[i]] > AVECECB){ dCBCE[[i]] <- '0' } else {dCBCE[[i]] <- '1'} + if(CE[[i]] > AVECECC){ dCECC[[i]] <- '0' } else {dCECC[[i]] <- '1'} + if(CC[[i]] > AVECECC){ dCCCE[[i]] <- '0' } else {dCCCE[[i]] <- '1'} + if(CE[[i]] > AVECECD){ dCECD[[i]] <- '0' } else {dCECD[[i]] <- '1'} + if(CD[[i]] > AVECECD){ dCDCE[[i]] <- '0' } else {dCDCE[[i]] <- '1'} + if(CE[[i]] > AVECECE){ dCECE[[i]] <- '0' } else {dCECE[[i]] <- '1'} + if(CE[[i]] > AVECECE){ dCECE[[i]] <- '0' } else {dCECE[[i]] <- '1'} + if(CE[[i]] > AVECECF){ dCECF[[i]] <- '0' } else {dCECF[[i]] <- '1'} + if(CF[[i]] > AVECECF){ dCFCE[[i]] <- '0' } else {dCFCE[[i]] <- '1'} + if(CE[[i]] > AVECECG){ dCECG[[i]] <- '0' } else {dCECG[[i]] <- '1'} + if(CG[[i]] > AVECECG){ dCGCE[[i]] <- '0' } else {dCGCE[[i]] <- '1'} + if(CE[[i]] > AVECECH){ dCECH[[i]] <- '0' } else {dCECH[[i]] <- '1'} + if(CH[[i]] > AVECECH){ dCHCE[[i]] <- '0' } else {dCHCE[[i]] <- '1'} + if(CF[[i]] > AVECFAA){ dCFAA[[i]] <- '0' } else {dCFAA[[i]] <- '1'} + if(AA[[i]] > AVECFAA){ dAACF[[i]] <- '0' } else {dAACF[[i]] <- '1'} + if(CF[[i]] > AVECFAB){ dCFAB[[i]] <- '0' } else {dCFAB[[i]] <- '1'} + if(AB[[i]] > AVECFAB){ dABCF[[i]] <- '0' } else {dABCF[[i]] <- '1'} + if(CF[[i]] > AVECFAC){ dCFAC[[i]] <- '0' } else {dCFAC[[i]] <- '1'} + if(AC[[i]] > AVECFAC){ dACCF[[i]] <- '0' } else {dACCF[[i]] <- '1'} + if(CF[[i]] > AVECFAD){ dCFAD[[i]] <- '0' } else {dCFAD[[i]] <- '1'} + if(AD[[i]] > AVECFAD){ dADCF[[i]] <- '0' } else {dADCF[[i]] <- '1'} + if(CF[[i]] > AVECFAE){ dCFAE[[i]] <- '0' } else {dCFAE[[i]] <- '1'} + if(AE[[i]] > AVECFAE){ dAECF[[i]] <- '0' } else {dAECF[[i]] <- '1'} + if(CF[[i]] > AVECFAF){ dCFAF[[i]] <- '0' } else {dCFAF[[i]] <- '1'} + if(AF[[i]] > AVECFAF){ dAFCF[[i]] <- '0' } else {dAFCF[[i]] <- '1'} + if(CF[[i]] > AVECFAG){ dCFAG[[i]] <- '0' } else {dCFAG[[i]] <- '1'} + if(AG[[i]] > AVECFAG){ dAGCF[[i]] <- '0' } else {dAGCF[[i]] <- '1'} + if(CF[[i]] > AVECFAH){ dCFAH[[i]] <- '0' } else {dCFAH[[i]] <- '1'} + if(AH[[i]] > AVECFAH){ dAHCF[[i]] <- '0' } else {dAHCF[[i]] <- '1'} + if(CF[[i]] > AVECFBA){ dCFBA[[i]] <- '0' } else {dCFBA[[i]] <- '1'} + if(BA[[i]] > AVECFBA){ dBACF[[i]] <- '0' } else {dBACF[[i]] <- '1'} + if(CF[[i]] > AVECFBB){ dCFBB[[i]] <- '0' } else {dCFBB[[i]] <- '1'} + if(BB[[i]] > AVECFBB){ dBBCF[[i]] <- '0' } else {dBBCF[[i]] <- '1'} + if(CF[[i]] > AVECFBC){ dCFBC[[i]] <- '0' } else {dCFBC[[i]] <- '1'} + if(BC[[i]] > AVECFBC){ dBCCF[[i]] <- '0' } else {dBCCF[[i]] <- '1'} + if(CF[[i]] > AVECFBD){ dCFBD[[i]] <- '0' } else {dCFBD[[i]] <- '1'} + if(BD[[i]] > AVECFBD){ dBDCF[[i]] <- '0' } else {dBDCF[[i]] <- '1'} + if(CF[[i]] > AVECFBE){ dCFBE[[i]] <- '0' } else {dCFBE[[i]] <- '1'} + if(BE[[i]] > AVECFBE){ dBECF[[i]] <- '0' } else {dBECF[[i]] <- '1'} + if(CF[[i]] > AVECFBF){ dCFBF[[i]] <- '0' } else {dCFBF[[i]] <- '1'} + if(BF[[i]] > AVECFBF){ dBFCF[[i]] <- '0' } else {dBFCF[[i]] <- '1'} + if(CF[[i]] > AVECFBG){ dCFBG[[i]] <- '0' } else {dCFBG[[i]] <- '1'} + if(BG[[i]] > AVECFBG){ dBGCF[[i]] <- '0' } else {dBGCF[[i]] <- '1'} + if(CF[[i]] > AVECFBH){ dCFBH[[i]] <- '0' } else {dCFBH[[i]] <- '1'} + if(BH[[i]] > AVECFBH){ dBHCF[[i]] <- '0' } else {dBHCF[[i]] <- '1'} + if(CF[[i]] > AVECFCA){ dCFCA[[i]] <- '0' } else {dCFCA[[i]] <- '1'} + if(CA[[i]] > AVECFCA){ dCACF[[i]] <- '0' } else {dCACF[[i]] <- '1'} + if(CF[[i]] > AVECFCB){ dCFCB[[i]] <- '0' } else {dCFCB[[i]] <- '1'} + if(CB[[i]] > AVECFCB){ dCBCF[[i]] <- '0' } else {dCBCF[[i]] <- '1'} + if(CF[[i]] > AVECFCC){ dCFCC[[i]] <- '0' } else {dCFCC[[i]] <- '1'} + if(CC[[i]] > AVECFCC){ dCCCF[[i]] <- '0' } else {dCCCF[[i]] <- '1'} + if(CF[[i]] > AVECFCD){ dCFCD[[i]] <- '0' } else {dCFCD[[i]] <- '1'} + if(CD[[i]] > AVECFCD){ dCDCF[[i]] <- '0' } else {dCDCF[[i]] <- '1'} + if(CF[[i]] > AVECFCE){ dCFCE[[i]] <- '0' } else {dCFCE[[i]] <- '1'} + if(CE[[i]] > AVECFCE){ dCECF[[i]] <- '0' } else {dCECF[[i]] <- '1'} + if(CF[[i]] > AVECFCF){ dCFCF[[i]] <- '0' } else {dCFCF[[i]] <- '1'} + if(CF[[i]] > AVECFCF){ dCFCF[[i]] <- '0' } else {dCFCF[[i]] <- '1'} + if(CF[[i]] > AVECFCG){ dCFCG[[i]] <- '0' } else {dCFCG[[i]] <- '1'} + if(CG[[i]] > AVECFCG){ dCGCF[[i]] <- '0' } else {dCGCF[[i]] <- '1'} + if(CF[[i]] > AVECFCH){ dCFCH[[i]] <- '0' } else {dCFCH[[i]] <- '1'} + if(CH[[i]] > AVECFCH){ dCHCF[[i]] <- '0' } else {dCHCF[[i]] <- '1'} + if(CG[[i]] > AVECGAA){ dCGAA[[i]] <- '0' } else {dCGAA[[i]] <- '1'} + if(AA[[i]] > AVECGAA){ dAACG[[i]] <- '0' } else {dAACG[[i]] <- '1'} + if(CG[[i]] > AVECGAB){ dCGAB[[i]] <- '0' } else {dCGAB[[i]] <- '1'} + if(AB[[i]] > AVECGAB){ dABCG[[i]] <- '0' } else {dABCG[[i]] <- '1'} + if(CG[[i]] > AVECGAC){ dCGAC[[i]] <- '0' } else {dCGAC[[i]] <- '1'} + if(AC[[i]] > AVECGAC){ dACCG[[i]] <- '0' } else {dACCG[[i]] <- '1'} + if(CG[[i]] > AVECGAD){ dCGAD[[i]] <- '0' } else {dCGAD[[i]] <- '1'} + if(AD[[i]] > AVECGAD){ dADCG[[i]] <- '0' } else {dADCG[[i]] <- '1'} + if(CG[[i]] > AVECGAE){ dCGAE[[i]] <- '0' } else {dCGAE[[i]] <- '1'} + if(AE[[i]] > AVECGAE){ dAECG[[i]] <- '0' } else {dAECG[[i]] <- '1'} + if(CG[[i]] > AVECGAF){ dCGAF[[i]] <- '0' } else {dCGAF[[i]] <- '1'} + if(AF[[i]] > AVECGAF){ dAFCG[[i]] <- '0' } else {dAFCG[[i]] <- '1'} + if(CG[[i]] > AVECGAG){ dCGAG[[i]] <- '0' } else {dCGAG[[i]] <- '1'} + if(AG[[i]] > AVECGAG){ dAGCG[[i]] <- '0' } else {dAGCG[[i]] <- '1'} + if(CG[[i]] > AVECGAH){ dCGAH[[i]] <- '0' } else {dCGAH[[i]] <- '1'} + if(AH[[i]] > AVECGAH){ dAHCG[[i]] <- '0' } else {dAHCG[[i]] <- '1'} + if(CG[[i]] > AVECGBA){ dCGBA[[i]] <- '0' } else {dCGBA[[i]] <- '1'} + if(BA[[i]] > AVECGBA){ dBACG[[i]] <- '0' } else {dBACG[[i]] <- '1'} + if(CG[[i]] > AVECGBB){ dCGBB[[i]] <- '0' } else {dCGBB[[i]] <- '1'} + if(BB[[i]] > AVECGBB){ dBBCG[[i]] <- '0' } else {dBBCG[[i]] <- '1'} + if(CG[[i]] > AVECGBC){ dCGBC[[i]] <- '0' } else {dCGBC[[i]] <- '1'} + if(BC[[i]] > AVECGBC){ dBCCG[[i]] <- '0' } else {dBCCG[[i]] <- '1'} + if(CG[[i]] > AVECGBD){ dCGBD[[i]] <- '0' } else {dCGBD[[i]] <- '1'} + if(BD[[i]] > AVECGBD){ dBDCG[[i]] <- '0' } else {dBDCG[[i]] <- '1'} + if(CG[[i]] > AVECGBE){ dCGBE[[i]] <- '0' } else {dCGBE[[i]] <- '1'} + if(BE[[i]] > AVECGBE){ dBECG[[i]] <- '0' } else {dBECG[[i]] <- '1'} + if(CG[[i]] > AVECGBF){ dCGBF[[i]] <- '0' } else {dCGBF[[i]] <- '1'} + if(BF[[i]] > AVECGBF){ dBFCG[[i]] <- '0' } else {dBFCG[[i]] <- '1'} + if(CG[[i]] > AVECGBG){ dCGBG[[i]] <- '0' } else {dCGBG[[i]] <- '1'} + if(BG[[i]] > AVECGBG){ dBGCG[[i]] <- '0' } else {dBGCG[[i]] <- '1'} + if(CG[[i]] > AVECGBH){ dCGBH[[i]] <- '0' } else {dCGBH[[i]] <- '1'} + if(BH[[i]] > AVECGBH){ dBHCG[[i]] <- '0' } else {dBHCG[[i]] <- '1'} + if(CG[[i]] > AVECGCA){ dCGCA[[i]] <- '0' } else {dCGCA[[i]] <- '1'} + if(CA[[i]] > AVECGCA){ dCACG[[i]] <- '0' } else {dCACG[[i]] <- '1'} + if(CG[[i]] > AVECGCB){ dCGCB[[i]] <- '0' } else {dCGCB[[i]] <- '1'} + if(CB[[i]] > AVECGCB){ dCBCG[[i]] <- '0' } else {dCBCG[[i]] <- '1'} + if(CG[[i]] > AVECGCC){ dCGCC[[i]] <- '0' } else {dCGCC[[i]] <- '1'} + if(CC[[i]] > AVECGCC){ dCCCG[[i]] <- '0' } else {dCCCG[[i]] <- '1'} + if(CG[[i]] > AVECGCD){ dCGCD[[i]] <- '0' } else {dCGCD[[i]] <- '1'} + if(CD[[i]] > AVECGCD){ dCDCG[[i]] <- '0' } else {dCDCG[[i]] <- '1'} + if(CG[[i]] > AVECGCE){ dCGCE[[i]] <- '0' } else {dCGCE[[i]] <- '1'} + if(CE[[i]] > AVECGCE){ dCECG[[i]] <- '0' } else {dCECG[[i]] <- '1'} + if(CG[[i]] > AVECGCF){ dCGCF[[i]] <- '0' } else {dCGCF[[i]] <- '1'} + if(CF[[i]] > AVECGCF){ dCFCG[[i]] <- '0' } else {dCFCG[[i]] <- '1'} + if(CG[[i]] > AVECGCG){ dCGCG[[i]] <- '0' } else {dCGCG[[i]] <- '1'} + if(CG[[i]] > AVECGCG){ dCGCG[[i]] <- '0' } else {dCGCG[[i]] <- '1'} + if(CG[[i]] > AVECGCH){ dCGCH[[i]] <- '0' } else {dCGCH[[i]] <- '1'} + if(CH[[i]] > AVECGCH){ dCHCG[[i]] <- '0' } else {dCHCG[[i]] <- '1'} + if(CH[[i]] > AVECHAA){ dCHAA[[i]] <- '0' } else {dCHAA[[i]] <- '1'} + if(AA[[i]] > AVECHAA){ dAACH[[i]] <- '0' } else {dAACH[[i]] <- '1'} + if(CH[[i]] > AVECHAB){ dCHAB[[i]] <- '0' } else {dCHAB[[i]] <- '1'} + if(AB[[i]] > AVECHAB){ dABCH[[i]] <- '0' } else {dABCH[[i]] <- '1'} + if(CH[[i]] > AVECHAC){ dCHAC[[i]] <- '0' } else {dCHAC[[i]] <- '1'} + if(AC[[i]] > AVECHAC){ dACCH[[i]] <- '0' } else {dACCH[[i]] <- '1'} + if(CH[[i]] > AVECHAD){ dCHAD[[i]] <- '0' } else {dCHAD[[i]] <- '1'} + if(AD[[i]] > AVECHAD){ dADCH[[i]] <- '0' } else {dADCH[[i]] <- '1'} + if(CH[[i]] > AVECHAE){ dCHAE[[i]] <- '0' } else {dCHAE[[i]] <- '1'} + if(AE[[i]] > AVECHAE){ dAECH[[i]] <- '0' } else {dAECH[[i]] <- '1'} + if(CH[[i]] > AVECHAF){ dCHAF[[i]] <- '0' } else {dCHAF[[i]] <- '1'} + if(AF[[i]] > AVECHAF){ dAFCH[[i]] <- '0' } else {dAFCH[[i]] <- '1'} + if(CH[[i]] > AVECHAG){ dCHAG[[i]] <- '0' } else {dCHAG[[i]] <- '1'} + if(AG[[i]] > AVECHAG){ dAGCH[[i]] <- '0' } else {dAGCH[[i]] <- '1'} + if(CH[[i]] > AVECHAH){ dCHAH[[i]] <- '0' } else {dCHAH[[i]] <- '1'} + if(AH[[i]] > AVECHAH){ dAHCH[[i]] <- '0' } else {dAHCH[[i]] <- '1'} + if(CH[[i]] > AVECHBA){ dCHBA[[i]] <- '0' } else {dCHBA[[i]] <- '1'} + if(BA[[i]] > AVECHBA){ dBACH[[i]] <- '0' } else {dBACH[[i]] <- '1'} + if(CH[[i]] > AVECHBB){ dCHBB[[i]] <- '0' } else {dCHBB[[i]] <- '1'} + if(BB[[i]] > AVECHBB){ dBBCH[[i]] <- '0' } else {dBBCH[[i]] <- '1'} + if(CH[[i]] > AVECHBC){ dCHBC[[i]] <- '0' } else {dCHBC[[i]] <- '1'} + if(BC[[i]] > AVECHBC){ dBCCH[[i]] <- '0' } else {dBCCH[[i]] <- '1'} + if(CH[[i]] > AVECHBD){ dCHBD[[i]] <- '0' } else {dCHBD[[i]] <- '1'} + if(BD[[i]] > AVECHBD){ dBDCH[[i]] <- '0' } else {dBDCH[[i]] <- '1'} + if(CH[[i]] > AVECHBE){ dCHBE[[i]] <- '0' } else {dCHBE[[i]] <- '1'} + if(BE[[i]] > AVECHBE){ dBECH[[i]] <- '0' } else {dBECH[[i]] <- '1'} + if(CH[[i]] > AVECHBF){ dCHBF[[i]] <- '0' } else {dCHBF[[i]] <- '1'} + if(BF[[i]] > AVECHBF){ dBFCH[[i]] <- '0' } else {dBFCH[[i]] <- '1'} + if(CH[[i]] > AVECHBG){ dCHBG[[i]] <- '0' } else {dCHBG[[i]] <- '1'} + if(BG[[i]] > AVECHBG){ dBGCH[[i]] <- '0' } else {dBGCH[[i]] <- '1'} + if(CH[[i]] > AVECHBH){ dCHBH[[i]] <- '0' } else {dCHBH[[i]] <- '1'} + if(BH[[i]] > AVECHBH){ dBHCH[[i]] <- '0' } else {dBHCH[[i]] <- '1'} + if(CH[[i]] > AVECHCA){ dCHCA[[i]] <- '0' } else {dCHCA[[i]] <- '1'} + if(CA[[i]] > AVECHCA){ dCACH[[i]] <- '0' } else {dCACH[[i]] <- '1'} + if(CH[[i]] > AVECHCB){ dCHCB[[i]] <- '0' } else {dCHCB[[i]] <- '1'} + if(CB[[i]] > AVECHCB){ dCBCH[[i]] <- '0' } else {dCBCH[[i]] <- '1'} + if(CH[[i]] > AVECHCC){ dCHCC[[i]] <- '0' } else {dCHCC[[i]] <- '1'} + if(CC[[i]] > AVECHCC){ dCCCH[[i]] <- '0' } else {dCCCH[[i]] <- '1'} + if(CH[[i]] > AVECHCD){ dCHCD[[i]] <- '0' } else {dCHCD[[i]] <- '1'} + if(CD[[i]] > AVECHCD){ dCDCH[[i]] <- '0' } else {dCDCH[[i]] <- '1'} + if(CH[[i]] > AVECHCE){ dCHCE[[i]] <- '0' } else {dCHCE[[i]] <- '1'} + if(CE[[i]] > AVECHCE){ dCECH[[i]] <- '0' } else {dCECH[[i]] <- '1'} + if(CH[[i]] > AVECHCF){ dCHCF[[i]] <- '0' } else {dCHCF[[i]] <- '1'} + if(CF[[i]] > AVECHCF){ dCFCH[[i]] <- '0' } else {dCFCH[[i]] <- '1'} + if(CH[[i]] > AVECHCG){ dCHCG[[i]] <- '0' } else {dCHCG[[i]] <- '1'} + if(CG[[i]] > AVECHCG){ dCGCH[[i]] <- '0' } else {dCGCH[[i]] <- '1'} + if(CH[[i]] > AVECHCH){ dCHCH[[i]] <- '0' } else {dCHCH[[i]] <- '1'} + if(CH[[i]] > AVECHCH){ dCHCH[[i]] <- '0' } else {dCHCH[[i]] <- '1'} + } > > > mcnemar.test(dAAAA, dAAAA, correct = F) McNemar's Chi-squared test data: dAAAA and dAAAA McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dAAAB, dABAA, correct = F) McNemar's Chi-squared test data: dAAAB and dABAA McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dAAAC, dACAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAAAD, dADAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAAAE, dAEAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAAAF, dAFAA, correct = F) McNemar's Chi-squared test data: dAAAF and dAFAA McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dAAAG, dAGAA, correct = F) McNemar's Chi-squared test data: dAAAG and dAGAA McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dAAAH, dAHAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAABA, dBAAA, correct = F) McNemar's Chi-squared test data: dAABA and dBAAA McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dAABB, dBBAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAABC, dBCAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAABD, dBDAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAABE, dBEAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAABF, dBFAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAABG, dBGAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAABH, dBHAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAACA, dCAAA, correct = F) McNemar's Chi-squared test data: dAACA and dCAAA McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dAACB, dCBAA, correct = F) McNemar's Chi-squared test data: dAACB and dCBAA McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dAACC, dCCAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAACD, dCDAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAACE, dCEAA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAACF, dCFAA, correct = F) McNemar's Chi-squared test data: dAACF and dCFAA McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dAACG, dCGAA, correct = F) McNemar's Chi-squared test data: dAACG and dCGAA McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dAACH, dCHAA, correct = F) McNemar's Chi-squared test data: dAACH and dCHAA McNemar's chi-squared = 0, df = 1, p-value = 1 > mcnemar.test(dABAA, dAAAB, correct = F) McNemar's Chi-squared test data: dABAA and dAAAB McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dABAB, dABAB, correct = F) McNemar's Chi-squared test data: dABAB and dABAB McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dABAC, dACAB, correct = F) McNemar's Chi-squared test data: dABAC and dACAB McNemar's chi-squared = 2.6667, df = 1, p-value = 0.1025 > mcnemar.test(dABAD, dADAB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dABAE, dAEAB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dABAF, dAFAB, correct = F) McNemar's Chi-squared test data: dABAF and dAFAB McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dABAG, dAGAB, correct = F) McNemar's Chi-squared test data: dABAG and dAGAB McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dABAH, dAHAB, correct = F) McNemar's Chi-squared test data: dABAH and dAHAB McNemar's chi-squared = 1.8, df = 1, p-value = 0.1797 > mcnemar.test(dABBA, dBAAB, correct = F) McNemar's Chi-squared test data: dABBA and dBAAB McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dABBB, dBBAB, correct = F) McNemar's Chi-squared test data: dABBB and dBBAB McNemar's chi-squared = 1.8, df = 1, p-value = 0.1797 > mcnemar.test(dABBC, dBCAB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dABBD, dBDAB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dABBE, dBEAB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dABBF, dBFAB, correct = F) McNemar's Chi-squared test data: dABBF and dBFAB McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dABBG, dBGAB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dABBH, dBHAB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dABCA, dCAAB, correct = F) McNemar's Chi-squared test data: dABCA and dCAAB McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dABCB, dCBAB, correct = F) McNemar's Chi-squared test data: dABCB and dCBAB McNemar's chi-squared = 2.6667, df = 1, p-value = 0.1025 > mcnemar.test(dABCC, dCCAB, correct = F) McNemar's Chi-squared test data: dABCC and dCCAB McNemar's chi-squared = 1.2857, df = 1, p-value = 0.2568 > mcnemar.test(dABCD, dCDAB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dABCE, dCEAB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dABCF, dCFAB, correct = F) McNemar's Chi-squared test data: dABCF and dCFAB McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dABCG, dCGAB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dABCH, dCHAB, correct = F) McNemar's Chi-squared test data: dABCH and dCHAB McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dACAA, dAAAC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dACAB, dABAC, correct = F) McNemar's Chi-squared test data: dACAB and dABAC McNemar's chi-squared = 2.6667, df = 1, p-value = 0.1025 > mcnemar.test(dACAC, dACAC, correct = F) McNemar's Chi-squared test data: dACAC and dACAC McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dACAD, dADAC, correct = F) McNemar's Chi-squared test data: dACAD and dADAC McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dACAE, dAEAC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dACAF, dAFAC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dACAG, dAGAC, correct = F) McNemar's Chi-squared test data: dACAG and dAGAC McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dACAH, dAHAC, correct = F) McNemar's Chi-squared test data: dACAH and dAHAC McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dACBA, dBAAC, correct = F) McNemar's Chi-squared test data: dACBA and dBAAC McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dACBB, dBBAC, correct = F) McNemar's Chi-squared test data: dACBB and dBBAC McNemar's chi-squared = 0, df = 1, p-value = 1 > mcnemar.test(dACBC, dBCAC, correct = F) McNemar's Chi-squared test data: dACBC and dBCAC McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dACBD, dBDAC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dACBE, dBEAC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dACBF, dBFAC, correct = F) McNemar's Chi-squared test data: dACBF and dBFAC McNemar's chi-squared = 0, df = 1, p-value = 1 > mcnemar.test(dACBG, dBGAC, correct = F) McNemar's Chi-squared test data: dACBG and dBGAC McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dACBH, dBHAC, correct = F) McNemar's Chi-squared test data: dACBH and dBHAC McNemar's chi-squared = 0, df = 1, p-value = 1 > mcnemar.test(dACCA, dCAAC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dACCB, dCBAC, correct = F) McNemar's Chi-squared test data: dACCB and dCBAC McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dACCC, dCCAC, correct = F) McNemar's Chi-squared test data: dACCC and dCCAC McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dACCD, dCDAC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dACCE, dCEAC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dACCF, dCFAC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dACCG, dCGAC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dACCH, dCHAC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADAA, dAAAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADAB, dABAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADAC, dACAD, correct = F) McNemar's Chi-squared test data: dADAC and dACAD McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dADAD, dADAD, correct = F) McNemar's Chi-squared test data: dADAD and dADAD McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dADAE, dAEAD, correct = F) McNemar's Chi-squared test data: dADAE and dAEAD McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dADAF, dAFAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADAG, dAGAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADAH, dAHAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADBA, dBAAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADBB, dBBAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADBC, dBCAD, correct = F) McNemar's Chi-squared test data: dADBC and dBCAD McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dADBD, dBDAD, correct = F) McNemar's Chi-squared test data: dADBD and dBDAD McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dADBE, dBEAD, correct = F) McNemar's Chi-squared test data: dADBE and dBEAD McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dADBF, dBFAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADBG, dBGAD, correct = F) McNemar's Chi-squared test data: dADBG and dBGAD McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dADBH, dBHAD, correct = F) McNemar's Chi-squared test data: dADBH and dBHAD McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dADCA, dCAAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADCB, dCBAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADCC, dCCAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADCD, dCDAD, correct = F) McNemar's Chi-squared test data: dADCD and dCDAD McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dADCE, dCEAD, correct = F) McNemar's Chi-squared test data: dADCE and dCEAD McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dADCF, dCFAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADCG, dCGAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dADCH, dCHAD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAEAA, dAAAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAEAB, dABAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAEAC, dACAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAEAD, dADAE, correct = F) McNemar's Chi-squared test data: dAEAD and dADAE McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dAEAE, dAEAE, correct = F) McNemar's Chi-squared test data: dAEAE and dAEAE McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dAEAF, dAFAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAEAG, dAGAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAEAH, dAHAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAEBA, dBAAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAEBB, dBBAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAEBC, dBCAE, correct = F) McNemar's Chi-squared test data: dAEBC and dBCAE McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dAEBD, dBDAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAEBE, dBEAE, correct = F) McNemar's Chi-squared test data: dAEBE and dBEAE McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dAEBF, dBFAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAEBG, dBGAE, correct = F) McNemar's Chi-squared test data: dAEBG and dBGAE McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dAEBH, dBHAE, correct = F) McNemar's Chi-squared test data: dAEBH and dBHAE McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dAECA, dCAAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAECB, dCBAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAECC, dCCAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAECD, dCDAE, correct = F) McNemar's Chi-squared test data: dAECD and dCDAE McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dAECE, dCEAE, correct = F) McNemar's Chi-squared test data: dAECE and dCEAE McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dAECF, dCFAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAECG, dCGAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAECH, dCHAE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAFAA, dAAAF, correct = F) McNemar's Chi-squared test data: dAFAA and dAAAF McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dAFAB, dABAF, correct = F) McNemar's Chi-squared test data: dAFAB and dABAF McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dAFAC, dACAF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAFAD, dADAF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAFAE, dAEAF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAFAF, dAFAF, correct = F) McNemar's Chi-squared test data: dAFAF and dAFAF McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dAFAG, dAGAF, correct = F) McNemar's Chi-squared test data: dAFAG and dAGAF McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dAFAH, dAHAF, correct = F) McNemar's Chi-squared test data: dAFAH and dAHAF McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dAFBA, dBAAF, correct = F) McNemar's Chi-squared test data: dAFBA and dBAAF McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dAFBB, dBBAF, correct = F) McNemar's Chi-squared test data: dAFBB and dBBAF McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dAFBC, dBCAF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAFBD, dBDAF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAFBE, dBEAF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAFBF, dBFAF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAFBG, dBGAF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAFBH, dBHAF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAFCA, dCAAF, correct = F) McNemar's Chi-squared test data: dAFCA and dCAAF McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dAFCB, dCBAF, correct = F) McNemar's Chi-squared test data: dAFCB and dCBAF McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dAFCC, dCCAF, correct = F) McNemar's Chi-squared test data: dAFCC and dCCAF McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dAFCD, dCDAF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAFCE, dCEAF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAFCF, dCFAF, correct = F) McNemar's Chi-squared test data: dAFCF and dCFAF McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dAFCG, dCGAF, correct = F) McNemar's Chi-squared test data: dAFCG and dCGAF McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dAFCH, dCHAF, correct = F) McNemar's Chi-squared test data: dAFCH and dCHAF McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dAGAA, dAAAG, correct = F) McNemar's Chi-squared test data: dAGAA and dAAAG McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dAGAB, dABAG, correct = F) McNemar's Chi-squared test data: dAGAB and dABAG McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dAGAC, dACAG, correct = F) McNemar's Chi-squared test data: dAGAC and dACAG McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dAGAD, dADAG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAGAE, dAEAG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAGAF, dAFAG, correct = F) McNemar's Chi-squared test data: dAGAF and dAFAG McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dAGAG, dAGAG, correct = F) McNemar's Chi-squared test data: dAGAG and dAGAG McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dAGAH, dAHAG, correct = F) McNemar's Chi-squared test data: dAGAH and dAHAG McNemar's chi-squared = 3.5714, df = 1, p-value = 0.05878 > mcnemar.test(dAGBA, dBAAG, correct = F) McNemar's Chi-squared test data: dAGBA and dBAAG McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dAGBB, dBBAG, correct = F) McNemar's Chi-squared test data: dAGBB and dBBAG McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dAGBC, dBCAG, correct = F) McNemar's Chi-squared test data: dAGBC and dBCAG McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dAGBD, dBDAG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAGBE, dBEAG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAGBF, dBFAG, correct = F) McNemar's Chi-squared test data: dAGBF and dBFAG McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dAGBG, dBGAG, correct = F) McNemar's Chi-squared test data: dAGBG and dBGAG McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dAGBH, dBHAG, correct = F) McNemar's Chi-squared test data: dAGBH and dBHAG McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dAGCA, dCAAG, correct = F) McNemar's Chi-squared test data: dAGCA and dCAAG McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dAGCB, dCBAG, correct = F) McNemar's Chi-squared test data: dAGCB and dCBAG McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dAGCC, dCCAG, correct = F) McNemar's Chi-squared test data: dAGCC and dCCAG McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dAGCD, dCDAG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAGCE, dCEAG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAGCF, dCFAG, correct = F) McNemar's Chi-squared test data: dAGCF and dCFAG McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dAGCG, dCGAG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAGCH, dCHAG, correct = F) McNemar's Chi-squared test data: dAGCH and dCHAG McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dAHAA, dAAAH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAHAB, dABAH, correct = F) McNemar's Chi-squared test data: dAHAB and dABAH McNemar's chi-squared = 1.8, df = 1, p-value = 0.1797 > mcnemar.test(dAHAC, dACAH, correct = F) McNemar's Chi-squared test data: dAHAC and dACAH McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dAHAD, dADAH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAHAE, dAEAH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAHAF, dAFAH, correct = F) McNemar's Chi-squared test data: dAHAF and dAFAH McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dAHAG, dAGAH, correct = F) McNemar's Chi-squared test data: dAHAG and dAGAH McNemar's chi-squared = 3.5714, df = 1, p-value = 0.05878 > mcnemar.test(dAHAH, dAHAH, correct = F) McNemar's Chi-squared test data: dAHAH and dAHAH McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dAHBA, dBAAH, correct = F) McNemar's Chi-squared test data: dAHBA and dBAAH McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dAHBB, dBBAH, correct = F) McNemar's Chi-squared test data: dAHBB and dBBAH McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dAHBC, dBCAH, correct = F) McNemar's Chi-squared test data: dAHBC and dBCAH McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dAHBD, dBDAH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAHBE, dBEAH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAHBF, dBFAH, correct = F) McNemar's Chi-squared test data: dAHBF and dBFAH McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dAHBG, dBGAH, correct = F) McNemar's Chi-squared test data: dAHBG and dBGAH McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dAHBH, dBHAH, correct = F) McNemar's Chi-squared test data: dAHBH and dBHAH McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dAHCA, dCAAH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAHCB, dCBAH, correct = F) McNemar's Chi-squared test data: dAHCB and dCBAH McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dAHCC, dCCAH, correct = F) McNemar's Chi-squared test data: dAHCC and dCCAH McNemar's chi-squared = 0.3333, df = 1, p-value = 0.5637 > mcnemar.test(dAHCD, dCDAH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAHCE, dCEAH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAHCF, dCFAH, correct = F) McNemar's Chi-squared test data: dAHCF and dCFAH McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dAHCG, dCGAH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAHCH, dCHAH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBAAA, dAABA, correct = F) McNemar's Chi-squared test data: dBAAA and dAABA McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dBAAB, dABBA, correct = F) McNemar's Chi-squared test data: dBAAB and dABBA McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dBAAC, dACBA, correct = F) McNemar's Chi-squared test data: dBAAC and dACBA McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dBAAD, dADBA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBAAE, dAEBA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBAAF, dAFBA, correct = F) McNemar's Chi-squared test data: dBAAF and dAFBA McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dBAAG, dAGBA, correct = F) McNemar's Chi-squared test data: dBAAG and dAGBA McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBAAH, dAHBA, correct = F) McNemar's Chi-squared test data: dBAAH and dAHBA McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dBABA, dBABA, correct = F) McNemar's Chi-squared test data: dBABA and dBABA McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dBABB, dBBBA, correct = F) McNemar's Chi-squared test data: dBABB and dBBBA McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dBABC, dBCBA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBABD, dBDBA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBABE, dBEBA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBABF, dBFBA, correct = F) McNemar's Chi-squared test data: dBABF and dBFBA McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dBABG, dBGBA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBABH, dBHBA, correct = F) McNemar's Chi-squared test data: dBABH and dBHBA McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dBACA, dCABA, correct = F) McNemar's Chi-squared test data: dBACA and dCABA McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBACB, dCBBA, correct = F) McNemar's Chi-squared test data: dBACB and dCBBA McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dBACC, dCCBA, correct = F) McNemar's Chi-squared test data: dBACC and dCCBA McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dBACD, dCDBA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBACE, dCEBA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBACF, dCFBA, correct = F) McNemar's Chi-squared test data: dBACF and dCFBA McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dBACG, dCGBA, correct = F) McNemar's Chi-squared test data: dBACG and dCGBA McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dBACH, dCHBA, correct = F) McNemar's Chi-squared test data: dBACH and dCHBA McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBBAA, dAABB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBBAB, dABBB, correct = F) McNemar's Chi-squared test data: dBBAB and dABBB McNemar's chi-squared = 1.8, df = 1, p-value = 0.1797 > mcnemar.test(dBBAC, dACBB, correct = F) McNemar's Chi-squared test data: dBBAC and dACBB McNemar's chi-squared = 0, df = 1, p-value = 1 > mcnemar.test(dBBAD, dADBB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBBAE, dAEBB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBBAF, dAFBB, correct = F) McNemar's Chi-squared test data: dBBAF and dAFBB McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dBBAG, dAGBB, correct = F) McNemar's Chi-squared test data: dBBAG and dAGBB McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBBAH, dAHBB, correct = F) McNemar's Chi-squared test data: dBBAH and dAHBB McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dBBBA, dBABB, correct = F) McNemar's Chi-squared test data: dBBBA and dBABB McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dBBBB, dBBBB, correct = F) McNemar's Chi-squared test data: dBBBB and dBBBB McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dBBBC, dBCBB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBBBD, dBDBB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBBBE, dBEBB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBBBF, dBFBB, correct = F) McNemar's Chi-squared test data: dBBBF and dBFBB McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dBBBG, dBGBB, correct = F) McNemar's Chi-squared test data: dBBBG and dBGBB McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBBBH, dBHBB, correct = F) McNemar's Chi-squared test data: dBBBH and dBHBB McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dBBCA, dCABB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBBCB, dCBBB, correct = F) McNemar's Chi-squared test data: dBBCB and dCBBB McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBBCC, dCCBB, correct = F) McNemar's Chi-squared test data: dBBCC and dCCBB McNemar's chi-squared = 0, df = 1, p-value = 1 > mcnemar.test(dBBCD, dCDBB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBBCE, dCEBB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBBCF, dCFBB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBBCG, dCGBB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBBCH, dCHBB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBCAA, dAABC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBCAB, dABBC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBCAC, dACBC, correct = F) McNemar's Chi-squared test data: dBCAC and dACBC McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBCAD, dADBC, correct = F) McNemar's Chi-squared test data: dBCAD and dADBC McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBCAE, dAEBC, correct = F) McNemar's Chi-squared test data: dBCAE and dAEBC McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dBCAF, dAFBC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBCAG, dAGBC, correct = F) McNemar's Chi-squared test data: dBCAG and dAGBC McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dBCAH, dAHBC, correct = F) McNemar's Chi-squared test data: dBCAH and dAHBC McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dBCBA, dBABC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBCBB, dBBBC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBCBC, dBCBC, correct = F) McNemar's Chi-squared test data: dBCBC and dBCBC McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dBCBD, dBDBC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBCBE, dBEBC, correct = F) McNemar's Chi-squared test data: dBCBE and dBEBC McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dBCBF, dBFBC, correct = F) McNemar's Chi-squared test data: dBCBF and dBFBC McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dBCBG, dBGBC, correct = F) McNemar's Chi-squared test data: dBCBG and dBGBC McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dBCBH, dBHBC, correct = F) McNemar's Chi-squared test data: dBCBH and dBHBC McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBCCA, dCABC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBCCB, dCBBC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBCCC, dCCBC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBCCD, dCDBC, correct = F) McNemar's Chi-squared test data: dBCCD and dCDBC McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dBCCE, dCEBC, correct = F) McNemar's Chi-squared test data: dBCCE and dCEBC McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBCCF, dCFBC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBCCG, dCGBC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBCCH, dCHBC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDAA, dAABD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDAB, dABBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDAC, dACBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDAD, dADBD, correct = F) McNemar's Chi-squared test data: dBDAD and dADBD McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBDAE, dAEBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDAF, dAFBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDAG, dAGBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDAH, dAHBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDBA, dBABD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDBB, dBBBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDBC, dBCBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDBD, dBDBD, correct = F) McNemar's Chi-squared test data: dBDBD and dBDBD McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dBDBE, dBEBD, correct = F) McNemar's Chi-squared test data: dBDBE and dBEBD McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dBDBF, dBFBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDBG, dBGBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDBH, dBHBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDCA, dCABD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDCB, dCBBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDCC, dCCBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDCD, dCDBD, correct = F) McNemar's Chi-squared test data: dBDCD and dCDBD McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dBDCE, dCEBD, correct = F) McNemar's Chi-squared test data: dBDCE and dCEBD McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBDCF, dCFBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDCG, dCGBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBDCH, dCHBD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBEAA, dAABE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBEAB, dABBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBEAC, dACBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBEAD, dADBE, correct = F) McNemar's Chi-squared test data: dBEAD and dADBE McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dBEAE, dAEBE, correct = F) McNemar's Chi-squared test data: dBEAE and dAEBE McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBEAF, dAFBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBEAG, dAGBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBEAH, dAHBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBEBA, dBABE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBEBB, dBBBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBEBC, dBCBE, correct = F) McNemar's Chi-squared test data: dBEBC and dBCBE McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dBEBD, dBDBE, correct = F) McNemar's Chi-squared test data: dBEBD and dBDBE McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dBEBE, dBEBE, correct = F) McNemar's Chi-squared test data: dBEBE and dBEBE McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dBEBF, dBFBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBEBG, dBGBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBEBH, dBHBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBECA, dCABE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBECB, dCBBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBECC, dCCBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBECD, dCDBE, correct = F) McNemar's Chi-squared test data: dBECD and dCDBE McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dBECE, dCEBE, correct = F) McNemar's Chi-squared test data: dBECE and dCEBE McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dBECF, dCFBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBECG, dCGBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBECH, dCHBE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBFAA, dAABF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBFAB, dABBF, correct = F) McNemar's Chi-squared test data: dBFAB and dABBF McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dBFAC, dACBF, correct = F) McNemar's Chi-squared test data: dBFAC and dACBF McNemar's chi-squared = 0, df = 1, p-value = 1 > mcnemar.test(dBFAD, dADBF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBFAE, dAEBF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBFAF, dAFBF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBFAG, dAGBF, correct = F) McNemar's Chi-squared test data: dBFAG and dAGBF McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dBFAH, dAHBF, correct = F) McNemar's Chi-squared test data: dBFAH and dAHBF McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBFBA, dBABF, correct = F) McNemar's Chi-squared test data: dBFBA and dBABF McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dBFBB, dBBBF, correct = F) McNemar's Chi-squared test data: dBFBB and dBBBF McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dBFBC, dBCBF, correct = F) McNemar's Chi-squared test data: dBFBC and dBCBF McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dBFBD, dBDBF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBFBE, dBEBF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBFBF, dBFBF, correct = F) McNemar's Chi-squared test data: dBFBF and dBFBF McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dBFBG, dBGBF, correct = F) McNemar's Chi-squared test data: dBFBG and dBGBF McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dBFBH, dBHBF, correct = F) McNemar's Chi-squared test data: dBFBH and dBHBF McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dBFCA, dCABF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBFCB, dCBBF, correct = F) McNemar's Chi-squared test data: dBFCB and dCBBF McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dBFCC, dCCBF, correct = F) McNemar's Chi-squared test data: dBFCC and dCCBF McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dBFCD, dCDBF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBFCE, dCEBF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBFCF, dCFBF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBFCG, dCGBF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBFCH, dCHBF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGAA, dAABG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGAB, dABBG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGAC, dACBG, correct = F) McNemar's Chi-squared test data: dBGAC and dACBG McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dBGAD, dADBG, correct = F) McNemar's Chi-squared test data: dBGAD and dADBG McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dBGAE, dAEBG, correct = F) McNemar's Chi-squared test data: dBGAE and dAEBG McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dBGAF, dAFBG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGAG, dAGBG, correct = F) McNemar's Chi-squared test data: dBGAG and dAGBG McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dBGAH, dAHBG, correct = F) McNemar's Chi-squared test data: dBGAH and dAHBG McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBGBA, dBABG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGBB, dBBBG, correct = F) McNemar's Chi-squared test data: dBGBB and dBBBG McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBGBC, dBCBG, correct = F) McNemar's Chi-squared test data: dBGBC and dBCBG McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dBGBD, dBDBG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGBE, dBEBG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGBF, dBFBG, correct = F) McNemar's Chi-squared test data: dBGBF and dBFBG McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dBGBG, dBGBG, correct = F) McNemar's Chi-squared test data: dBGBG and dBGBG McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dBGBH, dBHBG, correct = F) McNemar's Chi-squared test data: dBGBH and dBHBG McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dBGCA, dCABG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGCB, dCBBG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGCC, dCCBG, correct = F) McNemar's Chi-squared test data: dBGCC and dCCBG McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dBGCD, dCDBG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGCE, dCEBG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGCF, dCFBG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGCG, dCGBG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBGCH, dCHBG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBHAA, dAABH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBHAB, dABBH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBHAC, dACBH, correct = F) McNemar's Chi-squared test data: dBHAC and dACBH McNemar's chi-squared = 0, df = 1, p-value = 1 > mcnemar.test(dBHAD, dADBH, correct = F) McNemar's Chi-squared test data: dBHAD and dADBH McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dBHAE, dAEBH, correct = F) McNemar's Chi-squared test data: dBHAE and dAEBH McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dBHAF, dAFBH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBHAG, dAGBH, correct = F) McNemar's Chi-squared test data: dBHAG and dAGBH McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dBHAH, dAHBH, correct = F) McNemar's Chi-squared test data: dBHAH and dAHBH McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBHBA, dBABH, correct = F) McNemar's Chi-squared test data: dBHBA and dBABH McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dBHBB, dBBBH, correct = F) McNemar's Chi-squared test data: dBHBB and dBBBH McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dBHBC, dBCBH, correct = F) McNemar's Chi-squared test data: dBHBC and dBCBH McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dBHBD, dBDBH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBHBE, dBEBH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBHBF, dBFBH, correct = F) McNemar's Chi-squared test data: dBHBF and dBFBH McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dBHBG, dBGBH, correct = F) McNemar's Chi-squared test data: dBHBG and dBGBH McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dBHBH, dBHBH, correct = F) McNemar's Chi-squared test data: dBHBH and dBHBH McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dBHCA, dCABH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBHCB, dCBBH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBHCC, dCCBH, correct = F) McNemar's Chi-squared test data: dBHCC and dCCBH McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dBHCD, dCDBH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBHCE, dCEBH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBHCF, dCFBH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBHCG, dCGBH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dBHCH, dCHBH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCAAA, dAACA, correct = F) McNemar's Chi-squared test data: dCAAA and dAACA McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dCAAB, dABCA, correct = F) McNemar's Chi-squared test data: dCAAB and dABCA McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dCAAC, dACCA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCAAD, dADCA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCAAE, dAECA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCAAF, dAFCA, correct = F) McNemar's Chi-squared test data: dCAAF and dAFCA McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dCAAG, dAGCA, correct = F) McNemar's Chi-squared test data: dCAAG and dAGCA McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dCAAH, dAHCA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCABA, dBACA, correct = F) McNemar's Chi-squared test data: dCABA and dBACA McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dCABB, dBBCA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCABC, dBCCA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCABD, dBDCA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCABE, dBECA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCABF, dBFCA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCABG, dBGCA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCABH, dBHCA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCACA, dCACA, correct = F) McNemar's Chi-squared test data: dCACA and dCACA McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dCACB, dCBCA, correct = F) McNemar's Chi-squared test data: dCACB and dCBCA McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dCACC, dCCCA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCACD, dCDCA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCACE, dCECA, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCACF, dCFCA, correct = F) McNemar's Chi-squared test data: dCACF and dCFCA McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dCACG, dCGCA, correct = F) McNemar's Chi-squared test data: dCACG and dCGCA McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dCACH, dCHCA, correct = F) McNemar's Chi-squared test data: dCACH and dCHCA McNemar's chi-squared = 0.2, df = 1, p-value = 0.6547 > mcnemar.test(dCBAA, dAACB, correct = F) McNemar's Chi-squared test data: dCBAA and dAACB McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dCBAB, dABCB, correct = F) McNemar's Chi-squared test data: dCBAB and dABCB McNemar's chi-squared = 2.6667, df = 1, p-value = 0.1025 > mcnemar.test(dCBAC, dACCB, correct = F) McNemar's Chi-squared test data: dCBAC and dACCB McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dCBAD, dADCB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCBAE, dAECB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCBAF, dAFCB, correct = F) McNemar's Chi-squared test data: dCBAF and dAFCB McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dCBAG, dAGCB, correct = F) McNemar's Chi-squared test data: dCBAG and dAGCB McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dCBAH, dAHCB, correct = F) McNemar's Chi-squared test data: dCBAH and dAHCB McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dCBBA, dBACB, correct = F) McNemar's Chi-squared test data: dCBBA and dBACB McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dCBBB, dBBCB, correct = F) McNemar's Chi-squared test data: dCBBB and dBBCB McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dCBBC, dBCCB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCBBD, dBDCB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCBBE, dBECB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCBBF, dBFCB, correct = F) McNemar's Chi-squared test data: dCBBF and dBFCB McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dCBBG, dBGCB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCBBH, dBHCB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCBCA, dCACB, correct = F) McNemar's Chi-squared test data: dCBCA and dCACB McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dCBCB, dCBCB, correct = F) McNemar's Chi-squared test data: dCBCB and dCBCB McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dCBCC, dCCCB, correct = F) McNemar's Chi-squared test data: dCBCC and dCCCB McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dCBCD, dCDCB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCBCE, dCECB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCBCF, dCFCB, correct = F) McNemar's Chi-squared test data: dCBCF and dCFCB McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dCBCG, dCGCB, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCBCH, dCHCB, correct = F) McNemar's Chi-squared test data: dCBCH and dCHCB McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dCCAA, dAACC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCCAB, dABCC, correct = F) McNemar's Chi-squared test data: dCCAB and dABCC McNemar's chi-squared = 1.2857, df = 1, p-value = 0.2568 > mcnemar.test(dCCAC, dACCC, correct = F) McNemar's Chi-squared test data: dCCAC and dACCC McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dCCAD, dADCC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCCAE, dAECC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCCAF, dAFCC, correct = F) McNemar's Chi-squared test data: dCCAF and dAFCC McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dCCAG, dAGCC, correct = F) McNemar's Chi-squared test data: dCCAG and dAGCC McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dCCAH, dAHCC, correct = F) McNemar's Chi-squared test data: dCCAH and dAHCC McNemar's chi-squared = 0.3333, df = 1, p-value = 0.5637 > mcnemar.test(dCCBA, dBACC, correct = F) McNemar's Chi-squared test data: dCCBA and dBACC McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dCCBB, dBBCC, correct = F) McNemar's Chi-squared test data: dCCBB and dBBCC McNemar's chi-squared = 0, df = 1, p-value = 1 > mcnemar.test(dCCBC, dBCCC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCCBD, dBDCC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCCBE, dBECC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCCBF, dBFCC, correct = F) McNemar's Chi-squared test data: dCCBF and dBFCC McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dCCBG, dBGCC, correct = F) McNemar's Chi-squared test data: dCCBG and dBGCC McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dCCBH, dBHCC, correct = F) McNemar's Chi-squared test data: dCCBH and dBHCC McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dCCCA, dCACC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCCCB, dCBCC, correct = F) McNemar's Chi-squared test data: dCCCB and dCBCC McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dCCCC, dCCCC, correct = F) McNemar's Chi-squared test data: dCCCC and dCCCC McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dCCCD, dCDCC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCCCE, dCECC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCCCF, dCFCC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCCCG, dCGCC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCCCH, dCHCC, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDAA, dAACD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDAB, dABCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDAC, dACCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDAD, dADCD, correct = F) McNemar's Chi-squared test data: dCDAD and dADCD McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dCDAE, dAECD, correct = F) McNemar's Chi-squared test data: dCDAE and dAECD McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > mcnemar.test(dCDAF, dAFCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDAG, dAGCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDAH, dAHCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDBA, dBACD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDBB, dBBCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDBC, dBCCD, correct = F) McNemar's Chi-squared test data: dCDBC and dBCCD McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dCDBD, dBDCD, correct = F) McNemar's Chi-squared test data: dCDBD and dBDCD McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dCDBE, dBECD, correct = F) McNemar's Chi-squared test data: dCDBE and dBECD McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dCDBF, dBFCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDBG, dBGCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDBH, dBHCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDCA, dCACD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDCB, dCBCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDCC, dCCCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDCD, dCDCD, correct = F) McNemar's Chi-squared test data: dCDCD and dCDCD McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dCDCE, dCECD, correct = F) McNemar's Chi-squared test data: dCDCE and dCECD McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dCDCF, dCFCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDCG, dCGCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCDCH, dCHCD, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCEAA, dAACE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCEAB, dABCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCEAC, dACCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCEAD, dADCE, correct = F) McNemar's Chi-squared test data: dCEAD and dADCE McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dCEAE, dAECE, correct = F) McNemar's Chi-squared test data: dCEAE and dAECE McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dCEAF, dAFCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCEAG, dAGCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCEAH, dAHCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCEBA, dBACE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCEBB, dBBCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCEBC, dBCCE, correct = F) McNemar's Chi-squared test data: dCEBC and dBCCE McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dCEBD, dBDCE, correct = F) McNemar's Chi-squared test data: dCEBD and dBDCE McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dCEBE, dBECE, correct = F) McNemar's Chi-squared test data: dCEBE and dBECE McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dCEBF, dBFCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCEBG, dBGCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCEBH, dBHCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCECA, dCACE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCECB, dCBCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCECC, dCCCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCECD, dCDCE, correct = F) McNemar's Chi-squared test data: dCECD and dCDCE McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dCECE, dCECE, correct = F) McNemar's Chi-squared test data: dCECE and dCECE McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dCECF, dCFCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCECG, dCGCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCECH, dCHCE, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFAA, dAACF, correct = F) McNemar's Chi-squared test data: dCFAA and dAACF McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dCFAB, dABCF, correct = F) McNemar's Chi-squared test data: dCFAB and dABCF McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dCFAC, dACCF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFAD, dADCF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFAE, dAECF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFAF, dAFCF, correct = F) McNemar's Chi-squared test data: dCFAF and dAFCF McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dCFAG, dAGCF, correct = F) McNemar's Chi-squared test data: dCFAG and dAGCF McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dCFAH, dAHCF, correct = F) McNemar's Chi-squared test data: dCFAH and dAHCF McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dCFBA, dBACF, correct = F) McNemar's Chi-squared test data: dCFBA and dBACF McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dCFBB, dBBCF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFBC, dBCCF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFBD, dBDCF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFBE, dBECF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFBF, dBFCF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFBG, dBGCF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFBH, dBHCF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFCA, dCACF, correct = F) McNemar's Chi-squared test data: dCFCA and dCACF McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dCFCB, dCBCF, correct = F) McNemar's Chi-squared test data: dCFCB and dCBCF McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dCFCC, dCCCF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFCD, dCDCF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFCE, dCECF, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCFCF, dCFCF, correct = F) McNemar's Chi-squared test data: dCFCF and dCFCF McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dCFCG, dCGCF, correct = F) McNemar's Chi-squared test data: dCFCG and dCGCF McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dCFCH, dCHCF, correct = F) McNemar's Chi-squared test data: dCFCH and dCHCF McNemar's chi-squared = 1.8, df = 1, p-value = 0.1797 > mcnemar.test(dCGAA, dAACG, correct = F) McNemar's Chi-squared test data: dCGAA and dAACG McNemar's chi-squared = 7, df = 1, p-value = 0.008151 > mcnemar.test(dCGAB, dABCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGAC, dACCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGAD, dADCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGAE, dAECG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGAF, dAFCG, correct = F) McNemar's Chi-squared test data: dCGAF and dAFCG McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dCGAG, dAGCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGAH, dAHCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGBA, dBACG, correct = F) McNemar's Chi-squared test data: dCGBA and dBACG McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dCGBB, dBBCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGBC, dBCCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGBD, dBDCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGBE, dBECG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGBF, dBFCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGBG, dBGCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGBH, dBHCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGCA, dCACG, correct = F) McNemar's Chi-squared test data: dCGCA and dCACG McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dCGCB, dCBCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGCC, dCCCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGCD, dCDCG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGCE, dCECG, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCGCF, dCFCG, correct = F) McNemar's Chi-squared test data: dCGCF and dCFCG McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dCGCG, dCGCG, correct = F) McNemar's Chi-squared test data: dCGCG and dCGCG McNemar's chi-squared = NaN, df = 1, p-value = NA > mcnemar.test(dCGCH, dCHCG, correct = F) McNemar's Chi-squared test data: dCGCH and dCHCG McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dCHAA, dAACH, correct = F) McNemar's Chi-squared test data: dCHAA and dAACH McNemar's chi-squared = 0, df = 1, p-value = 1 > mcnemar.test(dCHAB, dABCH, correct = F) McNemar's Chi-squared test data: dCHAB and dABCH McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dCHAC, dACCH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHAD, dADCH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHAE, dAECH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHAF, dAFCH, correct = F) McNemar's Chi-squared test data: dCHAF and dAFCH McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > mcnemar.test(dCHAG, dAGCH, correct = F) McNemar's Chi-squared test data: dCHAG and dAGCH McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dCHAH, dAHCH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHBA, dBACH, correct = F) McNemar's Chi-squared test data: dCHBA and dBACH McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > mcnemar.test(dCHBB, dBBCH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHBC, dBCCH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHBD, dBDCH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHBE, dBECH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHBF, dBFCH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHBG, dBGCH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHBH, dBHCH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHCA, dCACH, correct = F) McNemar's Chi-squared test data: dCHCA and dCACH McNemar's chi-squared = 0.2, df = 1, p-value = 0.6547 > mcnemar.test(dCHCB, dCBCH, correct = F) McNemar's Chi-squared test data: dCHCB and dCBCH McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dCHCC, dCCCH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHCD, dCDCH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHCE, dCECH, correct = F) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dCHCF, dCFCH, correct = F) McNemar's Chi-squared test data: dCHCF and dCFCH McNemar's chi-squared = 1.8, df = 1, p-value = 0.1797 > mcnemar.test(dCHCG, dCGCH, correct = F) McNemar's Chi-squared test data: dCHCG and dCGCH McNemar's chi-squared = 6, df = 1, p-value = 0.01431 > mcnemar.test(dCHCH, dCHCH, correct = F) McNemar's Chi-squared test data: dCHCH and dCHCH McNemar's chi-squared = NaN, df = 1, p-value = NA > > > > > > > > > > > for(i in 1:length(AA)){ + if(dAAAA[[i]] > dAAAA[[i]]){bAAAA <- bAAAA+1} + if(dAAAA[[i]] < dAAAA[[i]]){cAAAA <- cAAAA+1} + if(dAAAB[[i]] > dABAA[[i]]){bAAAB <- bAAAB+1} + if(dAAAB[[i]] < dABAA[[i]]){cAAAB <- cAAAB+1} + if(dAAAC[[i]] > dACAA[[i]]){bAAAC <- bAAAC+1} + if(dAAAC[[i]] < dACAA[[i]]){cAAAC <- cAAAC+1} + if(dAAAD[[i]] > dADAA[[i]]){bAAAD <- bAAAD+1} + if(dAAAD[[i]] < dADAA[[i]]){cAAAD <- cAAAD+1} + if(dAAAE[[i]] > dAEAA[[i]]){bAAAE <- bAAAE+1} + if(dAAAE[[i]] < dAEAA[[i]]){cAAAE <- cAAAE+1} + if(dAAAF[[i]] > dAFAA[[i]]){bAAAF <- bAAAF+1} + if(dAAAF[[i]] < dAFAA[[i]]){cAAAF <- cAAAF+1} + if(dAAAG[[i]] > dAGAA[[i]]){bAAAG <- bAAAG+1} + if(dAAAG[[i]] < dAGAA[[i]]){cAAAG <- cAAAG+1} + if(dAAAH[[i]] > dAHAA[[i]]){bAAAH <- bAAAH+1} + if(dAAAH[[i]] < dAHAA[[i]]){cAAAH <- cAAAH+1} + if(dAABA[[i]] > dBAAA[[i]]){bAABA <- bAABA+1} + if(dAABA[[i]] < dBAAA[[i]]){cAABA <- cAABA+1} + if(dAABB[[i]] > dBBAA[[i]]){bAABB <- bAABB+1} + if(dAABB[[i]] < dBBAA[[i]]){cAABB <- cAABB+1} + if(dAABC[[i]] > dBCAA[[i]]){bAABC <- bAABC+1} + if(dAABC[[i]] < dBCAA[[i]]){cAABC <- cAABC+1} + if(dAABD[[i]] > dBDAA[[i]]){bAABD <- bAABD+1} + if(dAABD[[i]] < dBDAA[[i]]){cAABD <- cAABD+1} + if(dAABE[[i]] > dBEAA[[i]]){bAABE <- bAABE+1} + if(dAABE[[i]] < dBEAA[[i]]){cAABE <- cAABE+1} + if(dAABF[[i]] > dBFAA[[i]]){bAABF <- bAABF+1} + if(dAABF[[i]] < dBFAA[[i]]){cAABF <- cAABF+1} + if(dAABG[[i]] > dBGAA[[i]]){bAABG <- bAABG+1} + if(dAABG[[i]] < dBGAA[[i]]){cAABG <- cAABG+1} + if(dAABH[[i]] > dBHAA[[i]]){bAABH <- bAABH+1} + if(dAABH[[i]] < dBHAA[[i]]){cAABH <- cAABH+1} + if(dAACA[[i]] > dCAAA[[i]]){bAACA <- bAACA+1} + if(dAACA[[i]] < dCAAA[[i]]){cAACA <- cAACA+1} + if(dAACB[[i]] > dCBAA[[i]]){bAACB <- bAACB+1} + if(dAACB[[i]] < dCBAA[[i]]){cAACB <- cAACB+1} + if(dAACC[[i]] > dCCAA[[i]]){bAACC <- bAACC+1} + if(dAACC[[i]] < dCCAA[[i]]){cAACC <- cAACC+1} + if(dAACD[[i]] > dCDAA[[i]]){bAACD <- bAACD+1} + if(dAACD[[i]] < dCDAA[[i]]){cAACD <- cAACD+1} + if(dAACE[[i]] > dCEAA[[i]]){bAACE <- bAACE+1} + if(dAACE[[i]] < dCEAA[[i]]){cAACE <- cAACE+1} + if(dAACF[[i]] > dCFAA[[i]]){bAACF <- bAACF+1} + if(dAACF[[i]] < dCFAA[[i]]){cAACF <- cAACF+1} + if(dAACG[[i]] > dCGAA[[i]]){bAACG <- bAACG+1} + if(dAACG[[i]] < dCGAA[[i]]){cAACG <- cAACG+1} + if(dAACH[[i]] > dCHAA[[i]]){bAACH <- bAACH+1} + if(dAACH[[i]] < dCHAA[[i]]){cAACH <- cAACH+1} + if(dABAA[[i]] > dAAAB[[i]]){bABAA <- bABAA+1} + if(dABAA[[i]] < dAAAB[[i]]){cABAA <- cABAA+1} + if(dABAB[[i]] > dABAB[[i]]){bABAB <- bABAB+1} + if(dABAB[[i]] < dABAB[[i]]){cABAB <- cABAB+1} + if(dABAC[[i]] > dACAB[[i]]){bABAC <- bABAC+1} + if(dABAC[[i]] < dACAB[[i]]){cABAC <- cABAC+1} + if(dABAD[[i]] > dADAB[[i]]){bABAD <- bABAD+1} + if(dABAD[[i]] < dADAB[[i]]){cABAD <- cABAD+1} + if(dABAE[[i]] > dAEAB[[i]]){bABAE <- bABAE+1} + if(dABAE[[i]] < dAEAB[[i]]){cABAE <- cABAE+1} + if(dABAF[[i]] > dAFAB[[i]]){bABAF <- bABAF+1} + if(dABAF[[i]] < dAFAB[[i]]){cABAF <- cABAF+1} + if(dABAG[[i]] > dAGAB[[i]]){bABAG <- bABAG+1} + if(dABAG[[i]] < dAGAB[[i]]){cABAG <- cABAG+1} + if(dABAH[[i]] > dAHAB[[i]]){bABAH <- bABAH+1} + if(dABAH[[i]] < dAHAB[[i]]){cABAH <- cABAH+1} + if(dABBA[[i]] > dBAAB[[i]]){bABBA <- bABBA+1} + if(dABBA[[i]] < dBAAB[[i]]){cABBA <- cABBA+1} + if(dABBB[[i]] > dBBAB[[i]]){bABBB <- bABBB+1} + if(dABBB[[i]] < dBBAB[[i]]){cABBB <- cABBB+1} + if(dABBC[[i]] > dBCAB[[i]]){bABBC <- bABBC+1} + if(dABBC[[i]] < dBCAB[[i]]){cABBC <- cABBC+1} + if(dABBD[[i]] > dBDAB[[i]]){bABBD <- bABBD+1} + if(dABBD[[i]] < dBDAB[[i]]){cABBD <- cABBD+1} + if(dABBE[[i]] > dBEAB[[i]]){bABBE <- bABBE+1} + if(dABBE[[i]] < dBEAB[[i]]){cABBE <- cABBE+1} + if(dABBF[[i]] > dBFAB[[i]]){bABBF <- bABBF+1} + if(dABBF[[i]] < dBFAB[[i]]){cABBF <- cABBF+1} + if(dABBG[[i]] > dBGAB[[i]]){bABBG <- bABBG+1} + if(dABBG[[i]] < dBGAB[[i]]){cABBG <- cABBG+1} + if(dABBH[[i]] > dBHAB[[i]]){bABBH <- bABBH+1} + if(dABBH[[i]] < dBHAB[[i]]){cABBH <- cABBH+1} + if(dABCA[[i]] > dCAAB[[i]]){bABCA <- bABCA+1} + if(dABCA[[i]] < dCAAB[[i]]){cABCA <- cABCA+1} + if(dABCB[[i]] > dCBAB[[i]]){bABCB <- bABCB+1} + if(dABCB[[i]] < dCBAB[[i]]){cABCB <- cABCB+1} + if(dABCC[[i]] > dCCAB[[i]]){bABCC <- bABCC+1} + if(dABCC[[i]] < dCCAB[[i]]){cABCC <- cABCC+1} + if(dABCD[[i]] > dCDAB[[i]]){bABCD <- bABCD+1} + if(dABCD[[i]] < dCDAB[[i]]){cABCD <- cABCD+1} + if(dABCE[[i]] > dCEAB[[i]]){bABCE <- bABCE+1} + if(dABCE[[i]] < dCEAB[[i]]){cABCE <- cABCE+1} + if(dABCF[[i]] > dCFAB[[i]]){bABCF <- bABCF+1} + if(dABCF[[i]] < dCFAB[[i]]){cABCF <- cABCF+1} + if(dABCG[[i]] > dCGAB[[i]]){bABCG <- bABCG+1} + if(dABCG[[i]] < dCGAB[[i]]){cABCG <- cABCG+1} + if(dABCH[[i]] > dCHAB[[i]]){bABCH <- bABCH+1} + if(dABCH[[i]] < dCHAB[[i]]){cABCH <- cABCH+1} + if(dACAA[[i]] > dAAAC[[i]]){bACAA <- bACAA+1} + if(dACAA[[i]] < dAAAC[[i]]){cACAA <- cACAA+1} + if(dACAB[[i]] > dABAC[[i]]){bACAB <- bACAB+1} + if(dACAB[[i]] < dABAC[[i]]){cACAB <- cACAB+1} + if(dACAC[[i]] > dACAC[[i]]){bACAC <- bACAC+1} + if(dACAC[[i]] < dACAC[[i]]){cACAC <- cACAC+1} + if(dACAD[[i]] > dADAC[[i]]){bACAD <- bACAD+1} + if(dACAD[[i]] < dADAC[[i]]){cACAD <- cACAD+1} + if(dACAE[[i]] > dAEAC[[i]]){bACAE <- bACAE+1} + if(dACAE[[i]] < dAEAC[[i]]){cACAE <- cACAE+1} + if(dACAF[[i]] > dAFAC[[i]]){bACAF <- bACAF+1} + if(dACAF[[i]] < dAFAC[[i]]){cACAF <- cACAF+1} + if(dACAG[[i]] > dAGAC[[i]]){bACAG <- bACAG+1} + if(dACAG[[i]] < dAGAC[[i]]){cACAG <- cACAG+1} + if(dACAH[[i]] > dAHAC[[i]]){bACAH <- bACAH+1} + if(dACAH[[i]] < dAHAC[[i]]){cACAH <- cACAH+1} + if(dACBA[[i]] > dBAAC[[i]]){bACBA <- bACBA+1} + if(dACBA[[i]] < dBAAC[[i]]){cACBA <- cACBA+1} + if(dACBB[[i]] > dBBAC[[i]]){bACBB <- bACBB+1} + if(dACBB[[i]] < dBBAC[[i]]){cACBB <- cACBB+1} + if(dACBC[[i]] > dBCAC[[i]]){bACBC <- bACBC+1} + if(dACBC[[i]] < dBCAC[[i]]){cACBC <- cACBC+1} + if(dACBD[[i]] > dBDAC[[i]]){bACBD <- bACBD+1} + if(dACBD[[i]] < dBDAC[[i]]){cACBD <- cACBD+1} + if(dACBE[[i]] > dBEAC[[i]]){bACBE <- bACBE+1} + if(dACBE[[i]] < dBEAC[[i]]){cACBE <- cACBE+1} + if(dACBF[[i]] > dBFAC[[i]]){bACBF <- bACBF+1} + if(dACBF[[i]] < dBFAC[[i]]){cACBF <- cACBF+1} + if(dACBG[[i]] > dBGAC[[i]]){bACBG <- bACBG+1} + if(dACBG[[i]] < dBGAC[[i]]){cACBG <- cACBG+1} + if(dACBH[[i]] > dBHAC[[i]]){bACBH <- bACBH+1} + if(dACBH[[i]] < dBHAC[[i]]){cACBH <- cACBH+1} + if(dACCA[[i]] > dCAAC[[i]]){bACCA <- bACCA+1} + if(dACCA[[i]] < dCAAC[[i]]){cACCA <- cACCA+1} + if(dACCB[[i]] > dCBAC[[i]]){bACCB <- bACCB+1} + if(dACCB[[i]] < dCBAC[[i]]){cACCB <- cACCB+1} + if(dACCC[[i]] > dCCAC[[i]]){bACCC <- bACCC+1} + if(dACCC[[i]] < dCCAC[[i]]){cACCC <- cACCC+1} + if(dACCD[[i]] > dCDAC[[i]]){bACCD <- bACCD+1} + if(dACCD[[i]] < dCDAC[[i]]){cACCD <- cACCD+1} + if(dACCE[[i]] > dCEAC[[i]]){bACCE <- bACCE+1} + if(dACCE[[i]] < dCEAC[[i]]){cACCE <- cACCE+1} + if(dACCF[[i]] > dCFAC[[i]]){bACCF <- bACCF+1} + if(dACCF[[i]] < dCFAC[[i]]){cACCF <- cACCF+1} + if(dACCG[[i]] > dCGAC[[i]]){bACCG <- bACCG+1} + if(dACCG[[i]] < dCGAC[[i]]){cACCG <- cACCG+1} + if(dACCH[[i]] > dCHAC[[i]]){bACCH <- bACCH+1} + if(dACCH[[i]] < dCHAC[[i]]){cACCH <- cACCH+1} + if(dADAA[[i]] > dAAAD[[i]]){bADAA <- bADAA+1} + if(dADAA[[i]] < dAAAD[[i]]){cADAA <- cADAA+1} + if(dADAB[[i]] > dABAD[[i]]){bADAB <- bADAB+1} + if(dADAB[[i]] < dABAD[[i]]){cADAB <- cADAB+1} + if(dADAC[[i]] > dACAD[[i]]){bADAC <- bADAC+1} + if(dADAC[[i]] < dACAD[[i]]){cADAC <- cADAC+1} + if(dADAD[[i]] > dADAD[[i]]){bADAD <- bADAD+1} + if(dADAD[[i]] < dADAD[[i]]){cADAD <- cADAD+1} + if(dADAE[[i]] > dAEAD[[i]]){bADAE <- bADAE+1} + if(dADAE[[i]] < dAEAD[[i]]){cADAE <- cADAE+1} + if(dADAF[[i]] > dAFAD[[i]]){bADAF <- bADAF+1} + if(dADAF[[i]] < dAFAD[[i]]){cADAF <- cADAF+1} + if(dADAG[[i]] > dAGAD[[i]]){bADAG <- bADAG+1} + if(dADAG[[i]] < dAGAD[[i]]){cADAG <- cADAG+1} + if(dADAH[[i]] > dAHAD[[i]]){bADAH <- bADAH+1} + if(dADAH[[i]] < dAHAD[[i]]){cADAH <- cADAH+1} + if(dADBA[[i]] > dBAAD[[i]]){bADBA <- bADBA+1} + if(dADBA[[i]] < dBAAD[[i]]){cADBA <- cADBA+1} + if(dADBB[[i]] > dBBAD[[i]]){bADBB <- bADBB+1} + if(dADBB[[i]] < dBBAD[[i]]){cADBB <- cADBB+1} + if(dADBC[[i]] > dBCAD[[i]]){bADBC <- bADBC+1} + if(dADBC[[i]] < dBCAD[[i]]){cADBC <- cADBC+1} + if(dADBD[[i]] > dBDAD[[i]]){bADBD <- bADBD+1} + if(dADBD[[i]] < dBDAD[[i]]){cADBD <- cADBD+1} + if(dADBE[[i]] > dBEAD[[i]]){bADBE <- bADBE+1} + if(dADBE[[i]] < dBEAD[[i]]){cADBE <- cADBE+1} + if(dADBF[[i]] > dBFAD[[i]]){bADBF <- bADBF+1} + if(dADBF[[i]] < dBFAD[[i]]){cADBF <- cADBF+1} + if(dADBG[[i]] > dBGAD[[i]]){bADBG <- bADBG+1} + if(dADBG[[i]] < dBGAD[[i]]){cADBG <- cADBG+1} + if(dADBH[[i]] > dBHAD[[i]]){bADBH <- bADBH+1} + if(dADBH[[i]] < dBHAD[[i]]){cADBH <- cADBH+1} + if(dADCA[[i]] > dCAAD[[i]]){bADCA <- bADCA+1} + if(dADCA[[i]] < dCAAD[[i]]){cADCA <- cADCA+1} + if(dADCB[[i]] > dCBAD[[i]]){bADCB <- bADCB+1} + if(dADCB[[i]] < dCBAD[[i]]){cADCB <- cADCB+1} + if(dADCC[[i]] > dCCAD[[i]]){bADCC <- bADCC+1} + if(dADCC[[i]] < dCCAD[[i]]){cADCC <- cADCC+1} + if(dADCD[[i]] > dCDAD[[i]]){bADCD <- bADCD+1} + if(dADCD[[i]] < dCDAD[[i]]){cADCD <- cADCD+1} + if(dADCE[[i]] > dCEAD[[i]]){bADCE <- bADCE+1} + if(dADCE[[i]] < dCEAD[[i]]){cADCE <- cADCE+1} + if(dADCF[[i]] > dCFAD[[i]]){bADCF <- bADCF+1} + if(dADCF[[i]] < dCFAD[[i]]){cADCF <- cADCF+1} + if(dADCG[[i]] > dCGAD[[i]]){bADCG <- bADCG+1} + if(dADCG[[i]] < dCGAD[[i]]){cADCG <- cADCG+1} + if(dADCH[[i]] > dCHAD[[i]]){bADCH <- bADCH+1} + if(dADCH[[i]] < dCHAD[[i]]){cADCH <- cADCH+1} + if(dAEAA[[i]] > dAAAE[[i]]){bAEAA <- bAEAA+1} + if(dAEAA[[i]] < dAAAE[[i]]){cAEAA <- cAEAA+1} + if(dAEAB[[i]] > dABAE[[i]]){bAEAB <- bAEAB+1} + if(dAEAB[[i]] < dABAE[[i]]){cAEAB <- cAEAB+1} + if(dAEAC[[i]] > dACAE[[i]]){bAEAC <- bAEAC+1} + if(dAEAC[[i]] < dACAE[[i]]){cAEAC <- cAEAC+1} + if(dAEAD[[i]] > dADAE[[i]]){bAEAD <- bAEAD+1} + if(dAEAD[[i]] < dADAE[[i]]){cAEAD <- cAEAD+1} + if(dAEAE[[i]] > dAEAE[[i]]){bAEAE <- bAEAE+1} + if(dAEAE[[i]] < dAEAE[[i]]){cAEAE <- cAEAE+1} + if(dAEAF[[i]] > dAFAE[[i]]){bAEAF <- bAEAF+1} + if(dAEAF[[i]] < dAFAE[[i]]){cAEAF <- cAEAF+1} + if(dAEAG[[i]] > dAGAE[[i]]){bAEAG <- bAEAG+1} + if(dAEAG[[i]] < dAGAE[[i]]){cAEAG <- cAEAG+1} + if(dAEAH[[i]] > dAHAE[[i]]){bAEAH <- bAEAH+1} + if(dAEAH[[i]] < dAHAE[[i]]){cAEAH <- cAEAH+1} + if(dAEBA[[i]] > dBAAE[[i]]){bAEBA <- bAEBA+1} + if(dAEBA[[i]] < dBAAE[[i]]){cAEBA <- cAEBA+1} + if(dAEBB[[i]] > dBBAE[[i]]){bAEBB <- bAEBB+1} + if(dAEBB[[i]] < dBBAE[[i]]){cAEBB <- cAEBB+1} + if(dAEBC[[i]] > dBCAE[[i]]){bAEBC <- bAEBC+1} + if(dAEBC[[i]] < dBCAE[[i]]){cAEBC <- cAEBC+1} + if(dAEBD[[i]] > dBDAE[[i]]){bAEBD <- bAEBD+1} + if(dAEBD[[i]] < dBDAE[[i]]){cAEBD <- cAEBD+1} + if(dAEBE[[i]] > dBEAE[[i]]){bAEBE <- bAEBE+1} + if(dAEBE[[i]] < dBEAE[[i]]){cAEBE <- cAEBE+1} + if(dAEBF[[i]] > dBFAE[[i]]){bAEBF <- bAEBF+1} + if(dAEBF[[i]] < dBFAE[[i]]){cAEBF <- cAEBF+1} + if(dAEBG[[i]] > dBGAE[[i]]){bAEBG <- bAEBG+1} + if(dAEBG[[i]] < dBGAE[[i]]){cAEBG <- cAEBG+1} + if(dAEBH[[i]] > dBHAE[[i]]){bAEBH <- bAEBH+1} + if(dAEBH[[i]] < dBHAE[[i]]){cAEBH <- cAEBH+1} + if(dAECA[[i]] > dCAAE[[i]]){bAECA <- bAECA+1} + if(dAECA[[i]] < dCAAE[[i]]){cAECA <- cAECA+1} + if(dAECB[[i]] > dCBAE[[i]]){bAECB <- bAECB+1} + if(dAECB[[i]] < dCBAE[[i]]){cAECB <- cAECB+1} + if(dAECC[[i]] > dCCAE[[i]]){bAECC <- bAECC+1} + if(dAECC[[i]] < dCCAE[[i]]){cAECC <- cAECC+1} + if(dAECD[[i]] > dCDAE[[i]]){bAECD <- bAECD+1} + if(dAECD[[i]] < dCDAE[[i]]){cAECD <- cAECD+1} + if(dAECE[[i]] > dCEAE[[i]]){bAECE <- bAECE+1} + if(dAECE[[i]] < dCEAE[[i]]){cAECE <- cAECE+1} + if(dAECF[[i]] > dCFAE[[i]]){bAECF <- bAECF+1} + if(dAECF[[i]] < dCFAE[[i]]){cAECF <- cAECF+1} + if(dAECG[[i]] > dCGAE[[i]]){bAECG <- bAECG+1} + if(dAECG[[i]] < dCGAE[[i]]){cAECG <- cAECG+1} + if(dAECH[[i]] > dCHAE[[i]]){bAECH <- bAECH+1} + if(dAECH[[i]] < dCHAE[[i]]){cAECH <- cAECH+1} + if(dAFAA[[i]] > dAAAF[[i]]){bAFAA <- bAFAA+1} + if(dAFAA[[i]] < dAAAF[[i]]){cAFAA <- cAFAA+1} + if(dAFAB[[i]] > dABAF[[i]]){bAFAB <- bAFAB+1} + if(dAFAB[[i]] < dABAF[[i]]){cAFAB <- cAFAB+1} + if(dAFAC[[i]] > dACAF[[i]]){bAFAC <- bAFAC+1} + if(dAFAC[[i]] < dACAF[[i]]){cAFAC <- cAFAC+1} + if(dAFAD[[i]] > dADAF[[i]]){bAFAD <- bAFAD+1} + if(dAFAD[[i]] < dADAF[[i]]){cAFAD <- cAFAD+1} + if(dAFAE[[i]] > dAEAF[[i]]){bAFAE <- bAFAE+1} + if(dAFAE[[i]] < dAEAF[[i]]){cAFAE <- cAFAE+1} + if(dAFAF[[i]] > dAFAF[[i]]){bAFAF <- bAFAF+1} + if(dAFAF[[i]] < dAFAF[[i]]){cAFAF <- cAFAF+1} + if(dAFAG[[i]] > dAGAF[[i]]){bAFAG <- bAFAG+1} + if(dAFAG[[i]] < dAGAF[[i]]){cAFAG <- cAFAG+1} + if(dAFAH[[i]] > dAHAF[[i]]){bAFAH <- bAFAH+1} + if(dAFAH[[i]] < dAHAF[[i]]){cAFAH <- cAFAH+1} + if(dAFBA[[i]] > dBAAF[[i]]){bAFBA <- bAFBA+1} + if(dAFBA[[i]] < dBAAF[[i]]){cAFBA <- cAFBA+1} + if(dAFBB[[i]] > dBBAF[[i]]){bAFBB <- bAFBB+1} + if(dAFBB[[i]] < dBBAF[[i]]){cAFBB <- cAFBB+1} + if(dAFBC[[i]] > dBCAF[[i]]){bAFBC <- bAFBC+1} + if(dAFBC[[i]] < dBCAF[[i]]){cAFBC <- cAFBC+1} + if(dAFBD[[i]] > dBDAF[[i]]){bAFBD <- bAFBD+1} + if(dAFBD[[i]] < dBDAF[[i]]){cAFBD <- cAFBD+1} + if(dAFBE[[i]] > dBEAF[[i]]){bAFBE <- bAFBE+1} + if(dAFBE[[i]] < dBEAF[[i]]){cAFBE <- cAFBE+1} + if(dAFBF[[i]] > dBFAF[[i]]){bAFBF <- bAFBF+1} + if(dAFBF[[i]] < dBFAF[[i]]){cAFBF <- cAFBF+1} + if(dAFBG[[i]] > dBGAF[[i]]){bAFBG <- bAFBG+1} + if(dAFBG[[i]] < dBGAF[[i]]){cAFBG <- cAFBG+1} + if(dAFBH[[i]] > dBHAF[[i]]){bAFBH <- bAFBH+1} + if(dAFBH[[i]] < dBHAF[[i]]){cAFBH <- cAFBH+1} + if(dAFCA[[i]] > dCAAF[[i]]){bAFCA <- bAFCA+1} + if(dAFCA[[i]] < dCAAF[[i]]){cAFCA <- cAFCA+1} + if(dAFCB[[i]] > dCBAF[[i]]){bAFCB <- bAFCB+1} + if(dAFCB[[i]] < dCBAF[[i]]){cAFCB <- cAFCB+1} + if(dAFCC[[i]] > dCCAF[[i]]){bAFCC <- bAFCC+1} + if(dAFCC[[i]] < dCCAF[[i]]){cAFCC <- cAFCC+1} + if(dAFCD[[i]] > dCDAF[[i]]){bAFCD <- bAFCD+1} + if(dAFCD[[i]] < dCDAF[[i]]){cAFCD <- cAFCD+1} + if(dAFCE[[i]] > dCEAF[[i]]){bAFCE <- bAFCE+1} + if(dAFCE[[i]] < dCEAF[[i]]){cAFCE <- cAFCE+1} + if(dAFCF[[i]] > dCFAF[[i]]){bAFCF <- bAFCF+1} + if(dAFCF[[i]] < dCFAF[[i]]){cAFCF <- cAFCF+1} + if(dAFCG[[i]] > dCGAF[[i]]){bAFCG <- bAFCG+1} + if(dAFCG[[i]] < dCGAF[[i]]){cAFCG <- cAFCG+1} + if(dAFCH[[i]] > dCHAF[[i]]){bAFCH <- bAFCH+1} + if(dAFCH[[i]] < dCHAF[[i]]){cAFCH <- cAFCH+1} + if(dAGAA[[i]] > dAAAG[[i]]){bAGAA <- bAGAA+1} + if(dAGAA[[i]] < dAAAG[[i]]){cAGAA <- cAGAA+1} + if(dAGAB[[i]] > dABAG[[i]]){bAGAB <- bAGAB+1} + if(dAGAB[[i]] < dABAG[[i]]){cAGAB <- cAGAB+1} + if(dAGAC[[i]] > dACAG[[i]]){bAGAC <- bAGAC+1} + if(dAGAC[[i]] < dACAG[[i]]){cAGAC <- cAGAC+1} + if(dAGAD[[i]] > dADAG[[i]]){bAGAD <- bAGAD+1} + if(dAGAD[[i]] < dADAG[[i]]){cAGAD <- cAGAD+1} + if(dAGAE[[i]] > dAEAG[[i]]){bAGAE <- bAGAE+1} + if(dAGAE[[i]] < dAEAG[[i]]){cAGAE <- cAGAE+1} + if(dAGAF[[i]] > dAFAG[[i]]){bAGAF <- bAGAF+1} + if(dAGAF[[i]] < dAFAG[[i]]){cAGAF <- cAGAF+1} + if(dAGAG[[i]] > dAGAG[[i]]){bAGAG <- bAGAG+1} + if(dAGAG[[i]] < dAGAG[[i]]){cAGAG <- cAGAG+1} + if(dAGAH[[i]] > dAHAG[[i]]){bAGAH <- bAGAH+1} + if(dAGAH[[i]] < dAHAG[[i]]){cAGAH <- cAGAH+1} + if(dAGBA[[i]] > dBAAG[[i]]){bAGBA <- bAGBA+1} + if(dAGBA[[i]] < dBAAG[[i]]){cAGBA <- cAGBA+1} + if(dAGBB[[i]] > dBBAG[[i]]){bAGBB <- bAGBB+1} + if(dAGBB[[i]] < dBBAG[[i]]){cAGBB <- cAGBB+1} + if(dAGBC[[i]] > dBCAG[[i]]){bAGBC <- bAGBC+1} + if(dAGBC[[i]] < dBCAG[[i]]){cAGBC <- cAGBC+1} + if(dAGBD[[i]] > dBDAG[[i]]){bAGBD <- bAGBD+1} + if(dAGBD[[i]] < dBDAG[[i]]){cAGBD <- cAGBD+1} + if(dAGBE[[i]] > dBEAG[[i]]){bAGBE <- bAGBE+1} + if(dAGBE[[i]] < dBEAG[[i]]){cAGBE <- cAGBE+1} + if(dAGBF[[i]] > dBFAG[[i]]){bAGBF <- bAGBF+1} + if(dAGBF[[i]] < dBFAG[[i]]){cAGBF <- cAGBF+1} + if(dAGBG[[i]] > dBGAG[[i]]){bAGBG <- bAGBG+1} + if(dAGBG[[i]] < dBGAG[[i]]){cAGBG <- cAGBG+1} + if(dAGBH[[i]] > dBHAG[[i]]){bAGBH <- bAGBH+1} + if(dAGBH[[i]] < dBHAG[[i]]){cAGBH <- cAGBH+1} + if(dAGCA[[i]] > dCAAG[[i]]){bAGCA <- bAGCA+1} + if(dAGCA[[i]] < dCAAG[[i]]){cAGCA <- cAGCA+1} + if(dAGCB[[i]] > dCBAG[[i]]){bAGCB <- bAGCB+1} + if(dAGCB[[i]] < dCBAG[[i]]){cAGCB <- cAGCB+1} + if(dAGCC[[i]] > dCCAG[[i]]){bAGCC <- bAGCC+1} + if(dAGCC[[i]] < dCCAG[[i]]){cAGCC <- cAGCC+1} + if(dAGCD[[i]] > dCDAG[[i]]){bAGCD <- bAGCD+1} + if(dAGCD[[i]] < dCDAG[[i]]){cAGCD <- cAGCD+1} + if(dAGCE[[i]] > dCEAG[[i]]){bAGCE <- bAGCE+1} + if(dAGCE[[i]] < dCEAG[[i]]){cAGCE <- cAGCE+1} + if(dAGCF[[i]] > dCFAG[[i]]){bAGCF <- bAGCF+1} + if(dAGCF[[i]] < dCFAG[[i]]){cAGCF <- cAGCF+1} + if(dAGCG[[i]] > dCGAG[[i]]){bAGCG <- bAGCG+1} + if(dAGCG[[i]] < dCGAG[[i]]){cAGCG <- cAGCG+1} + if(dAGCH[[i]] > dCHAG[[i]]){bAGCH <- bAGCH+1} + if(dAGCH[[i]] < dCHAG[[i]]){cAGCH <- cAGCH+1} + if(dAHAA[[i]] > dAAAH[[i]]){bAHAA <- bAHAA+1} + if(dAHAA[[i]] < dAAAH[[i]]){cAHAA <- cAHAA+1} + if(dAHAB[[i]] > dABAH[[i]]){bAHAB <- bAHAB+1} + if(dAHAB[[i]] < dABAH[[i]]){cAHAB <- cAHAB+1} + if(dAHAC[[i]] > dACAH[[i]]){bAHAC <- bAHAC+1} + if(dAHAC[[i]] < dACAH[[i]]){cAHAC <- cAHAC+1} + if(dAHAD[[i]] > dADAH[[i]]){bAHAD <- bAHAD+1} + if(dAHAD[[i]] < dADAH[[i]]){cAHAD <- cAHAD+1} + if(dAHAE[[i]] > dAEAH[[i]]){bAHAE <- bAHAE+1} + if(dAHAE[[i]] < dAEAH[[i]]){cAHAE <- cAHAE+1} + if(dAHAF[[i]] > dAFAH[[i]]){bAHAF <- bAHAF+1} + if(dAHAF[[i]] < dAFAH[[i]]){cAHAF <- cAHAF+1} + if(dAHAG[[i]] > dAGAH[[i]]){bAHAG <- bAHAG+1} + if(dAHAG[[i]] < dAGAH[[i]]){cAHAG <- cAHAG+1} + if(dAHAH[[i]] > dAHAH[[i]]){bAHAH <- bAHAH+1} + if(dAHAH[[i]] < dAHAH[[i]]){cAHAH <- cAHAH+1} + if(dAHBA[[i]] > dBAAH[[i]]){bAHBA <- bAHBA+1} + if(dAHBA[[i]] < dBAAH[[i]]){cAHBA <- cAHBA+1} + if(dAHBB[[i]] > dBBAH[[i]]){bAHBB <- bAHBB+1} + if(dAHBB[[i]] < dBBAH[[i]]){cAHBB <- cAHBB+1} + if(dAHBC[[i]] > dBCAH[[i]]){bAHBC <- bAHBC+1} + if(dAHBC[[i]] < dBCAH[[i]]){cAHBC <- cAHBC+1} + if(dAHBD[[i]] > dBDAH[[i]]){bAHBD <- bAHBD+1} + if(dAHBD[[i]] < dBDAH[[i]]){cAHBD <- cAHBD+1} + if(dAHBE[[i]] > dBEAH[[i]]){bAHBE <- bAHBE+1} + if(dAHBE[[i]] < dBEAH[[i]]){cAHBE <- cAHBE+1} + if(dAHBF[[i]] > dBFAH[[i]]){bAHBF <- bAHBF+1} + if(dAHBF[[i]] < dBFAH[[i]]){cAHBF <- cAHBF+1} + if(dAHBG[[i]] > dBGAH[[i]]){bAHBG <- bAHBG+1} + if(dAHBG[[i]] < dBGAH[[i]]){cAHBG <- cAHBG+1} + if(dAHBH[[i]] > dBHAH[[i]]){bAHBH <- bAHBH+1} + if(dAHBH[[i]] < dBHAH[[i]]){cAHBH <- cAHBH+1} + if(dAHCA[[i]] > dCAAH[[i]]){bAHCA <- bAHCA+1} + if(dAHCA[[i]] < dCAAH[[i]]){cAHCA <- cAHCA+1} + if(dAHCB[[i]] > dCBAH[[i]]){bAHCB <- bAHCB+1} + if(dAHCB[[i]] < dCBAH[[i]]){cAHCB <- cAHCB+1} + if(dAHCC[[i]] > dCCAH[[i]]){bAHCC <- bAHCC+1} + if(dAHCC[[i]] < dCCAH[[i]]){cAHCC <- cAHCC+1} + if(dAHCD[[i]] > dCDAH[[i]]){bAHCD <- bAHCD+1} + if(dAHCD[[i]] < dCDAH[[i]]){cAHCD <- cAHCD+1} + if(dAHCE[[i]] > dCEAH[[i]]){bAHCE <- bAHCE+1} + if(dAHCE[[i]] < dCEAH[[i]]){cAHCE <- cAHCE+1} + if(dAHCF[[i]] > dCFAH[[i]]){bAHCF <- bAHCF+1} + if(dAHCF[[i]] < dCFAH[[i]]){cAHCF <- cAHCF+1} + if(dAHCG[[i]] > dCGAH[[i]]){bAHCG <- bAHCG+1} + if(dAHCG[[i]] < dCGAH[[i]]){cAHCG <- cAHCG+1} + if(dAHCH[[i]] > dCHAH[[i]]){bAHCH <- bAHCH+1} + if(dAHCH[[i]] < dCHAH[[i]]){cAHCH <- cAHCH+1} + if(dBAAA[[i]] > dAABA[[i]]){bBAAA <- bBAAA+1} + if(dBAAA[[i]] < dAABA[[i]]){cBAAA <- cBAAA+1} + if(dBAAB[[i]] > dABBA[[i]]){bBAAB <- bBAAB+1} + if(dBAAB[[i]] < dABBA[[i]]){cBAAB <- cBAAB+1} + if(dBAAC[[i]] > dACBA[[i]]){bBAAC <- bBAAC+1} + if(dBAAC[[i]] < dACBA[[i]]){cBAAC <- cBAAC+1} + if(dBAAD[[i]] > dADBA[[i]]){bBAAD <- bBAAD+1} + if(dBAAD[[i]] < dADBA[[i]]){cBAAD <- cBAAD+1} + if(dBAAE[[i]] > dAEBA[[i]]){bBAAE <- bBAAE+1} + if(dBAAE[[i]] < dAEBA[[i]]){cBAAE <- cBAAE+1} + if(dBAAF[[i]] > dAFBA[[i]]){bBAAF <- bBAAF+1} + if(dBAAF[[i]] < dAFBA[[i]]){cBAAF <- cBAAF+1} + if(dBAAG[[i]] > dAGBA[[i]]){bBAAG <- bBAAG+1} + if(dBAAG[[i]] < dAGBA[[i]]){cBAAG <- cBAAG+1} + if(dBAAH[[i]] > dAHBA[[i]]){bBAAH <- bBAAH+1} + if(dBAAH[[i]] < dAHBA[[i]]){cBAAH <- cBAAH+1} + if(dBABA[[i]] > dBABA[[i]]){bBABA <- bBABA+1} + if(dBABA[[i]] < dBABA[[i]]){cBABA <- cBABA+1} + if(dBABB[[i]] > dBBBA[[i]]){bBABB <- bBABB+1} + if(dBABB[[i]] < dBBBA[[i]]){cBABB <- cBABB+1} + if(dBABC[[i]] > dBCBA[[i]]){bBABC <- bBABC+1} + if(dBABC[[i]] < dBCBA[[i]]){cBABC <- cBABC+1} + if(dBABD[[i]] > dBDBA[[i]]){bBABD <- bBABD+1} + if(dBABD[[i]] < dBDBA[[i]]){cBABD <- cBABD+1} + if(dBABE[[i]] > dBEBA[[i]]){bBABE <- bBABE+1} + if(dBABE[[i]] < dBEBA[[i]]){cBABE <- cBABE+1} + if(dBABF[[i]] > dBFBA[[i]]){bBABF <- bBABF+1} + if(dBABF[[i]] < dBFBA[[i]]){cBABF <- cBABF+1} + if(dBABG[[i]] > dBGBA[[i]]){bBABG <- bBABG+1} + if(dBABG[[i]] < dBGBA[[i]]){cBABG <- cBABG+1} + if(dBABH[[i]] > dBHBA[[i]]){bBABH <- bBABH+1} + if(dBABH[[i]] < dBHBA[[i]]){cBABH <- cBABH+1} + if(dBACA[[i]] > dCABA[[i]]){bBACA <- bBACA+1} + if(dBACA[[i]] < dCABA[[i]]){cBACA <- cBACA+1} + if(dBACB[[i]] > dCBBA[[i]]){bBACB <- bBACB+1} + if(dBACB[[i]] < dCBBA[[i]]){cBACB <- cBACB+1} + if(dBACC[[i]] > dCCBA[[i]]){bBACC <- bBACC+1} + if(dBACC[[i]] < dCCBA[[i]]){cBACC <- cBACC+1} + if(dBACD[[i]] > dCDBA[[i]]){bBACD <- bBACD+1} + if(dBACD[[i]] < dCDBA[[i]]){cBACD <- cBACD+1} + if(dBACE[[i]] > dCEBA[[i]]){bBACE <- bBACE+1} + if(dBACE[[i]] < dCEBA[[i]]){cBACE <- cBACE+1} + if(dBACF[[i]] > dCFBA[[i]]){bBACF <- bBACF+1} + if(dBACF[[i]] < dCFBA[[i]]){cBACF <- cBACF+1} + if(dBACG[[i]] > dCGBA[[i]]){bBACG <- bBACG+1} + if(dBACG[[i]] < dCGBA[[i]]){cBACG <- cBACG+1} + if(dBACH[[i]] > dCHBA[[i]]){bBACH <- bBACH+1} + if(dBACH[[i]] < dCHBA[[i]]){cBACH <- cBACH+1} + if(dBBAA[[i]] > dAABB[[i]]){bBBAA <- bBBAA+1} + if(dBBAA[[i]] < dAABB[[i]]){cBBAA <- cBBAA+1} + if(dBBAB[[i]] > dABBB[[i]]){bBBAB <- bBBAB+1} + if(dBBAB[[i]] < dABBB[[i]]){cBBAB <- cBBAB+1} + if(dBBAC[[i]] > dACBB[[i]]){bBBAC <- bBBAC+1} + if(dBBAC[[i]] < dACBB[[i]]){cBBAC <- cBBAC+1} + if(dBBAD[[i]] > dADBB[[i]]){bBBAD <- bBBAD+1} + if(dBBAD[[i]] < dADBB[[i]]){cBBAD <- cBBAD+1} + if(dBBAE[[i]] > dAEBB[[i]]){bBBAE <- bBBAE+1} + if(dBBAE[[i]] < dAEBB[[i]]){cBBAE <- cBBAE+1} + if(dBBAF[[i]] > dAFBB[[i]]){bBBAF <- bBBAF+1} + if(dBBAF[[i]] < dAFBB[[i]]){cBBAF <- cBBAF+1} + if(dBBAG[[i]] > dAGBB[[i]]){bBBAG <- bBBAG+1} + if(dBBAG[[i]] < dAGBB[[i]]){cBBAG <- cBBAG+1} + if(dBBAH[[i]] > dAHBB[[i]]){bBBAH <- bBBAH+1} + if(dBBAH[[i]] < dAHBB[[i]]){cBBAH <- cBBAH+1} + if(dBBBA[[i]] > dBABB[[i]]){bBBBA <- bBBBA+1} + if(dBBBA[[i]] < dBABB[[i]]){cBBBA <- cBBBA+1} + if(dBBBB[[i]] > dBBBB[[i]]){bBBBB <- bBBBB+1} + if(dBBBB[[i]] < dBBBB[[i]]){cBBBB <- cBBBB+1} + if(dBBBC[[i]] > dBCBB[[i]]){bBBBC <- bBBBC+1} + if(dBBBC[[i]] < dBCBB[[i]]){cBBBC <- cBBBC+1} + if(dBBBD[[i]] > dBDBB[[i]]){bBBBD <- bBBBD+1} + if(dBBBD[[i]] < dBDBB[[i]]){cBBBD <- cBBBD+1} + if(dBBBE[[i]] > dBEBB[[i]]){bBBBE <- bBBBE+1} + if(dBBBE[[i]] < dBEBB[[i]]){cBBBE <- cBBBE+1} + if(dBBBF[[i]] > dBFBB[[i]]){bBBBF <- bBBBF+1} + if(dBBBF[[i]] < dBFBB[[i]]){cBBBF <- cBBBF+1} + if(dBBBG[[i]] > dBGBB[[i]]){bBBBG <- bBBBG+1} + if(dBBBG[[i]] < dBGBB[[i]]){cBBBG <- cBBBG+1} + if(dBBBH[[i]] > dBHBB[[i]]){bBBBH <- bBBBH+1} + if(dBBBH[[i]] < dBHBB[[i]]){cBBBH <- cBBBH+1} + if(dBBCA[[i]] > dCABB[[i]]){bBBCA <- bBBCA+1} + if(dBBCA[[i]] < dCABB[[i]]){cBBCA <- cBBCA+1} + if(dBBCB[[i]] > dCBBB[[i]]){bBBCB <- bBBCB+1} + if(dBBCB[[i]] < dCBBB[[i]]){cBBCB <- cBBCB+1} + if(dBBCC[[i]] > dCCBB[[i]]){bBBCC <- bBBCC+1} + if(dBBCC[[i]] < dCCBB[[i]]){cBBCC <- cBBCC+1} + if(dBBCD[[i]] > dCDBB[[i]]){bBBCD <- bBBCD+1} + if(dBBCD[[i]] < dCDBB[[i]]){cBBCD <- cBBCD+1} + if(dBBCE[[i]] > dCEBB[[i]]){bBBCE <- bBBCE+1} + if(dBBCE[[i]] < dCEBB[[i]]){cBBCE <- cBBCE+1} + if(dBBCF[[i]] > dCFBB[[i]]){bBBCF <- bBBCF+1} + if(dBBCF[[i]] < dCFBB[[i]]){cBBCF <- cBBCF+1} + if(dBBCG[[i]] > dCGBB[[i]]){bBBCG <- bBBCG+1} + if(dBBCG[[i]] < dCGBB[[i]]){cBBCG <- cBBCG+1} + if(dBBCH[[i]] > dCHBB[[i]]){bBBCH <- bBBCH+1} + if(dBBCH[[i]] < dCHBB[[i]]){cBBCH <- cBBCH+1} + if(dBCAA[[i]] > dAABC[[i]]){bBCAA <- bBCAA+1} + if(dBCAA[[i]] < dAABC[[i]]){cBCAA <- cBCAA+1} + if(dBCAB[[i]] > dABBC[[i]]){bBCAB <- bBCAB+1} + if(dBCAB[[i]] < dABBC[[i]]){cBCAB <- cBCAB+1} + if(dBCAC[[i]] > dACBC[[i]]){bBCAC <- bBCAC+1} + if(dBCAC[[i]] < dACBC[[i]]){cBCAC <- cBCAC+1} + if(dBCAD[[i]] > dADBC[[i]]){bBCAD <- bBCAD+1} + if(dBCAD[[i]] < dADBC[[i]]){cBCAD <- cBCAD+1} + if(dBCAE[[i]] > dAEBC[[i]]){bBCAE <- bBCAE+1} + if(dBCAE[[i]] < dAEBC[[i]]){cBCAE <- cBCAE+1} + if(dBCAF[[i]] > dAFBC[[i]]){bBCAF <- bBCAF+1} + if(dBCAF[[i]] < dAFBC[[i]]){cBCAF <- cBCAF+1} + if(dBCAG[[i]] > dAGBC[[i]]){bBCAG <- bBCAG+1} + if(dBCAG[[i]] < dAGBC[[i]]){cBCAG <- cBCAG+1} + if(dBCAH[[i]] > dAHBC[[i]]){bBCAH <- bBCAH+1} + if(dBCAH[[i]] < dAHBC[[i]]){cBCAH <- cBCAH+1} + if(dBCBA[[i]] > dBABC[[i]]){bBCBA <- bBCBA+1} + if(dBCBA[[i]] < dBABC[[i]]){cBCBA <- cBCBA+1} + if(dBCBB[[i]] > dBBBC[[i]]){bBCBB <- bBCBB+1} + if(dBCBB[[i]] < dBBBC[[i]]){cBCBB <- cBCBB+1} + if(dBCBC[[i]] > dBCBC[[i]]){bBCBC <- bBCBC+1} + if(dBCBC[[i]] < dBCBC[[i]]){cBCBC <- cBCBC+1} + if(dBCBD[[i]] > dBDBC[[i]]){bBCBD <- bBCBD+1} + if(dBCBD[[i]] < dBDBC[[i]]){cBCBD <- cBCBD+1} + if(dBCBE[[i]] > dBEBC[[i]]){bBCBE <- bBCBE+1} + if(dBCBE[[i]] < dBEBC[[i]]){cBCBE <- cBCBE+1} + if(dBCBF[[i]] > dBFBC[[i]]){bBCBF <- bBCBF+1} + if(dBCBF[[i]] < dBFBC[[i]]){cBCBF <- cBCBF+1} + if(dBCBG[[i]] > dBGBC[[i]]){bBCBG <- bBCBG+1} + if(dBCBG[[i]] < dBGBC[[i]]){cBCBG <- cBCBG+1} + if(dBCBH[[i]] > dBHBC[[i]]){bBCBH <- bBCBH+1} + if(dBCBH[[i]] < dBHBC[[i]]){cBCBH <- cBCBH+1} + if(dBCCA[[i]] > dCABC[[i]]){bBCCA <- bBCCA+1} + if(dBCCA[[i]] < dCABC[[i]]){cBCCA <- cBCCA+1} + if(dBCCB[[i]] > dCBBC[[i]]){bBCCB <- bBCCB+1} + if(dBCCB[[i]] < dCBBC[[i]]){cBCCB <- cBCCB+1} + if(dBCCC[[i]] > dCCBC[[i]]){bBCCC <- bBCCC+1} + if(dBCCC[[i]] < dCCBC[[i]]){cBCCC <- cBCCC+1} + if(dBCCD[[i]] > dCDBC[[i]]){bBCCD <- bBCCD+1} + if(dBCCD[[i]] < dCDBC[[i]]){cBCCD <- cBCCD+1} + if(dBCCE[[i]] > dCEBC[[i]]){bBCCE <- bBCCE+1} + if(dBCCE[[i]] < dCEBC[[i]]){cBCCE <- cBCCE+1} + if(dBCCF[[i]] > dCFBC[[i]]){bBCCF <- bBCCF+1} + if(dBCCF[[i]] < dCFBC[[i]]){cBCCF <- cBCCF+1} + if(dBCCG[[i]] > dCGBC[[i]]){bBCCG <- bBCCG+1} + if(dBCCG[[i]] < dCGBC[[i]]){cBCCG <- cBCCG+1} + if(dBCCH[[i]] > dCHBC[[i]]){bBCCH <- bBCCH+1} + if(dBCCH[[i]] < dCHBC[[i]]){cBCCH <- cBCCH+1} + if(dBDAA[[i]] > dAABD[[i]]){bBDAA <- bBDAA+1} + if(dBDAA[[i]] < dAABD[[i]]){cBDAA <- cBDAA+1} + if(dBDAB[[i]] > dABBD[[i]]){bBDAB <- bBDAB+1} + if(dBDAB[[i]] < dABBD[[i]]){cBDAB <- cBDAB+1} + if(dBDAC[[i]] > dACBD[[i]]){bBDAC <- bBDAC+1} + if(dBDAC[[i]] < dACBD[[i]]){cBDAC <- cBDAC+1} + if(dBDAD[[i]] > dADBD[[i]]){bBDAD <- bBDAD+1} + if(dBDAD[[i]] < dADBD[[i]]){cBDAD <- cBDAD+1} + if(dBDAE[[i]] > dAEBD[[i]]){bBDAE <- bBDAE+1} + if(dBDAE[[i]] < dAEBD[[i]]){cBDAE <- cBDAE+1} + if(dBDAF[[i]] > dAFBD[[i]]){bBDAF <- bBDAF+1} + if(dBDAF[[i]] < dAFBD[[i]]){cBDAF <- cBDAF+1} + if(dBDAG[[i]] > dAGBD[[i]]){bBDAG <- bBDAG+1} + if(dBDAG[[i]] < dAGBD[[i]]){cBDAG <- cBDAG+1} + if(dBDAH[[i]] > dAHBD[[i]]){bBDAH <- bBDAH+1} + if(dBDAH[[i]] < dAHBD[[i]]){cBDAH <- cBDAH+1} + if(dBDBA[[i]] > dBABD[[i]]){bBDBA <- bBDBA+1} + if(dBDBA[[i]] < dBABD[[i]]){cBDBA <- cBDBA+1} + if(dBDBB[[i]] > dBBBD[[i]]){bBDBB <- bBDBB+1} + if(dBDBB[[i]] < dBBBD[[i]]){cBDBB <- cBDBB+1} + if(dBDBC[[i]] > dBCBD[[i]]){bBDBC <- bBDBC+1} + if(dBDBC[[i]] < dBCBD[[i]]){cBDBC <- cBDBC+1} + if(dBDBD[[i]] > dBDBD[[i]]){bBDBD <- bBDBD+1} + if(dBDBD[[i]] < dBDBD[[i]]){cBDBD <- cBDBD+1} + if(dBDBE[[i]] > dBEBD[[i]]){bBDBE <- bBDBE+1} + if(dBDBE[[i]] < dBEBD[[i]]){cBDBE <- cBDBE+1} + if(dBDBF[[i]] > dBFBD[[i]]){bBDBF <- bBDBF+1} + if(dBDBF[[i]] < dBFBD[[i]]){cBDBF <- cBDBF+1} + if(dBDBG[[i]] > dBGBD[[i]]){bBDBG <- bBDBG+1} + if(dBDBG[[i]] < dBGBD[[i]]){cBDBG <- cBDBG+1} + if(dBDBH[[i]] > dBHBD[[i]]){bBDBH <- bBDBH+1} + if(dBDBH[[i]] < dBHBD[[i]]){cBDBH <- cBDBH+1} + if(dBDCA[[i]] > dCABD[[i]]){bBDCA <- bBDCA+1} + if(dBDCA[[i]] < dCABD[[i]]){cBDCA <- cBDCA+1} + if(dBDCB[[i]] > dCBBD[[i]]){bBDCB <- bBDCB+1} + if(dBDCB[[i]] < dCBBD[[i]]){cBDCB <- cBDCB+1} + if(dBDCC[[i]] > dCCBD[[i]]){bBDCC <- bBDCC+1} + if(dBDCC[[i]] < dCCBD[[i]]){cBDCC <- cBDCC+1} + if(dBDCD[[i]] > dCDBD[[i]]){bBDCD <- bBDCD+1} + if(dBDCD[[i]] < dCDBD[[i]]){cBDCD <- cBDCD+1} + if(dBDCE[[i]] > dCEBD[[i]]){bBDCE <- bBDCE+1} + if(dBDCE[[i]] < dCEBD[[i]]){cBDCE <- cBDCE+1} + if(dBDCF[[i]] > dCFBD[[i]]){bBDCF <- bBDCF+1} + if(dBDCF[[i]] < dCFBD[[i]]){cBDCF <- cBDCF+1} + if(dBDCG[[i]] > dCGBD[[i]]){bBDCG <- bBDCG+1} + if(dBDCG[[i]] < dCGBD[[i]]){cBDCG <- cBDCG+1} + if(dBDCH[[i]] > dCHBD[[i]]){bBDCH <- bBDCH+1} + if(dBDCH[[i]] < dCHBD[[i]]){cBDCH <- cBDCH+1} + if(dBEAA[[i]] > dAABE[[i]]){bBEAA <- bBEAA+1} + if(dBEAA[[i]] < dAABE[[i]]){cBEAA <- cBEAA+1} + if(dBEAB[[i]] > dABBE[[i]]){bBEAB <- bBEAB+1} + if(dBEAB[[i]] < dABBE[[i]]){cBEAB <- cBEAB+1} + if(dBEAC[[i]] > dACBE[[i]]){bBEAC <- bBEAC+1} + if(dBEAC[[i]] < dACBE[[i]]){cBEAC <- cBEAC+1} + if(dBEAD[[i]] > dADBE[[i]]){bBEAD <- bBEAD+1} + if(dBEAD[[i]] < dADBE[[i]]){cBEAD <- cBEAD+1} + if(dBEAE[[i]] > dAEBE[[i]]){bBEAE <- bBEAE+1} + if(dBEAE[[i]] < dAEBE[[i]]){cBEAE <- cBEAE+1} + if(dBEAF[[i]] > dAFBE[[i]]){bBEAF <- bBEAF+1} + if(dBEAF[[i]] < dAFBE[[i]]){cBEAF <- cBEAF+1} + if(dBEAG[[i]] > dAGBE[[i]]){bBEAG <- bBEAG+1} + if(dBEAG[[i]] < dAGBE[[i]]){cBEAG <- cBEAG+1} + if(dBEAH[[i]] > dAHBE[[i]]){bBEAH <- bBEAH+1} + if(dBEAH[[i]] < dAHBE[[i]]){cBEAH <- cBEAH+1} + if(dBEBA[[i]] > dBABE[[i]]){bBEBA <- bBEBA+1} + if(dBEBA[[i]] < dBABE[[i]]){cBEBA <- cBEBA+1} + if(dBEBB[[i]] > dBBBE[[i]]){bBEBB <- bBEBB+1} + if(dBEBB[[i]] < dBBBE[[i]]){cBEBB <- cBEBB+1} + if(dBEBC[[i]] > dBCBE[[i]]){bBEBC <- bBEBC+1} + if(dBEBC[[i]] < dBCBE[[i]]){cBEBC <- cBEBC+1} + if(dBEBD[[i]] > dBDBE[[i]]){bBEBD <- bBEBD+1} + if(dBEBD[[i]] < dBDBE[[i]]){cBEBD <- cBEBD+1} + if(dBEBE[[i]] > dBEBE[[i]]){bBEBE <- bBEBE+1} + if(dBEBE[[i]] < dBEBE[[i]]){cBEBE <- cBEBE+1} + if(dBEBF[[i]] > dBFBE[[i]]){bBEBF <- bBEBF+1} + if(dBEBF[[i]] < dBFBE[[i]]){cBEBF <- cBEBF+1} + if(dBEBG[[i]] > dBGBE[[i]]){bBEBG <- bBEBG+1} + if(dBEBG[[i]] < dBGBE[[i]]){cBEBG <- cBEBG+1} + if(dBEBH[[i]] > dBHBE[[i]]){bBEBH <- bBEBH+1} + if(dBEBH[[i]] < dBHBE[[i]]){cBEBH <- cBEBH+1} + if(dBECA[[i]] > dCABE[[i]]){bBECA <- bBECA+1} + if(dBECA[[i]] < dCABE[[i]]){cBECA <- cBECA+1} + if(dBECB[[i]] > dCBBE[[i]]){bBECB <- bBECB+1} + if(dBECB[[i]] < dCBBE[[i]]){cBECB <- cBECB+1} + if(dBECC[[i]] > dCCBE[[i]]){bBECC <- bBECC+1} + if(dBECC[[i]] < dCCBE[[i]]){cBECC <- cBECC+1} + if(dBECD[[i]] > dCDBE[[i]]){bBECD <- bBECD+1} + if(dBECD[[i]] < dCDBE[[i]]){cBECD <- cBECD+1} + if(dBECE[[i]] > dCEBE[[i]]){bBECE <- bBECE+1} + if(dBECE[[i]] < dCEBE[[i]]){cBECE <- cBECE+1} + if(dBECF[[i]] > dCFBE[[i]]){bBECF <- bBECF+1} + if(dBECF[[i]] < dCFBE[[i]]){cBECF <- cBECF+1} + if(dBECG[[i]] > dCGBE[[i]]){bBECG <- bBECG+1} + if(dBECG[[i]] < dCGBE[[i]]){cBECG <- cBECG+1} + if(dBECH[[i]] > dCHBE[[i]]){bBECH <- bBECH+1} + if(dBECH[[i]] < dCHBE[[i]]){cBECH <- cBECH+1} + if(dBFAA[[i]] > dAABF[[i]]){bBFAA <- bBFAA+1} + if(dBFAA[[i]] < dAABF[[i]]){cBFAA <- cBFAA+1} + if(dBFAB[[i]] > dABBF[[i]]){bBFAB <- bBFAB+1} + if(dBFAB[[i]] < dABBF[[i]]){cBFAB <- cBFAB+1} + if(dBFAC[[i]] > dACBF[[i]]){bBFAC <- bBFAC+1} + if(dBFAC[[i]] < dACBF[[i]]){cBFAC <- cBFAC+1} + if(dBFAD[[i]] > dADBF[[i]]){bBFAD <- bBFAD+1} + if(dBFAD[[i]] < dADBF[[i]]){cBFAD <- cBFAD+1} + if(dBFAE[[i]] > dAEBF[[i]]){bBFAE <- bBFAE+1} + if(dBFAE[[i]] < dAEBF[[i]]){cBFAE <- cBFAE+1} + if(dBFAF[[i]] > dAFBF[[i]]){bBFAF <- bBFAF+1} + if(dBFAF[[i]] < dAFBF[[i]]){cBFAF <- cBFAF+1} + if(dBFAG[[i]] > dAGBF[[i]]){bBFAG <- bBFAG+1} + if(dBFAG[[i]] < dAGBF[[i]]){cBFAG <- cBFAG+1} + if(dBFAH[[i]] > dAHBF[[i]]){bBFAH <- bBFAH+1} + if(dBFAH[[i]] < dAHBF[[i]]){cBFAH <- cBFAH+1} + if(dBFBA[[i]] > dBABF[[i]]){bBFBA <- bBFBA+1} + if(dBFBA[[i]] < dBABF[[i]]){cBFBA <- cBFBA+1} + if(dBFBB[[i]] > dBBBF[[i]]){bBFBB <- bBFBB+1} + if(dBFBB[[i]] < dBBBF[[i]]){cBFBB <- cBFBB+1} + if(dBFBC[[i]] > dBCBF[[i]]){bBFBC <- bBFBC+1} + if(dBFBC[[i]] < dBCBF[[i]]){cBFBC <- cBFBC+1} + if(dBFBD[[i]] > dBDBF[[i]]){bBFBD <- bBFBD+1} + if(dBFBD[[i]] < dBDBF[[i]]){cBFBD <- cBFBD+1} + if(dBFBE[[i]] > dBEBF[[i]]){bBFBE <- bBFBE+1} + if(dBFBE[[i]] < dBEBF[[i]]){cBFBE <- cBFBE+1} + if(dBFBF[[i]] > dBFBF[[i]]){bBFBF <- bBFBF+1} + if(dBFBF[[i]] < dBFBF[[i]]){cBFBF <- cBFBF+1} + if(dBFBG[[i]] > dBGBF[[i]]){bBFBG <- bBFBG+1} + if(dBFBG[[i]] < dBGBF[[i]]){cBFBG <- cBFBG+1} + if(dBFBH[[i]] > dBHBF[[i]]){bBFBH <- bBFBH+1} + if(dBFBH[[i]] < dBHBF[[i]]){cBFBH <- cBFBH+1} + if(dBFCA[[i]] > dCABF[[i]]){bBFCA <- bBFCA+1} + if(dBFCA[[i]] < dCABF[[i]]){cBFCA <- cBFCA+1} + if(dBFCB[[i]] > dCBBF[[i]]){bBFCB <- bBFCB+1} + if(dBFCB[[i]] < dCBBF[[i]]){cBFCB <- cBFCB+1} + if(dBFCC[[i]] > dCCBF[[i]]){bBFCC <- bBFCC+1} + if(dBFCC[[i]] < dCCBF[[i]]){cBFCC <- cBFCC+1} + if(dBFCD[[i]] > dCDBF[[i]]){bBFCD <- bBFCD+1} + if(dBFCD[[i]] < dCDBF[[i]]){cBFCD <- cBFCD+1} + if(dBFCE[[i]] > dCEBF[[i]]){bBFCE <- bBFCE+1} + if(dBFCE[[i]] < dCEBF[[i]]){cBFCE <- cBFCE+1} + if(dBFCF[[i]] > dCFBF[[i]]){bBFCF <- bBFCF+1} + if(dBFCF[[i]] < dCFBF[[i]]){cBFCF <- cBFCF+1} + if(dBFCG[[i]] > dCGBF[[i]]){bBFCG <- bBFCG+1} + if(dBFCG[[i]] < dCGBF[[i]]){cBFCG <- cBFCG+1} + if(dBFCH[[i]] > dCHBF[[i]]){bBFCH <- bBFCH+1} + if(dBFCH[[i]] < dCHBF[[i]]){cBFCH <- cBFCH+1} + if(dBGAA[[i]] > dAABG[[i]]){bBGAA <- bBGAA+1} + if(dBGAA[[i]] < dAABG[[i]]){cBGAA <- cBGAA+1} + if(dBGAB[[i]] > dABBG[[i]]){bBGAB <- bBGAB+1} + if(dBGAB[[i]] < dABBG[[i]]){cBGAB <- cBGAB+1} + if(dBGAC[[i]] > dACBG[[i]]){bBGAC <- bBGAC+1} + if(dBGAC[[i]] < dACBG[[i]]){cBGAC <- cBGAC+1} + if(dBGAD[[i]] > dADBG[[i]]){bBGAD <- bBGAD+1} + if(dBGAD[[i]] < dADBG[[i]]){cBGAD <- cBGAD+1} + if(dBGAE[[i]] > dAEBG[[i]]){bBGAE <- bBGAE+1} + if(dBGAE[[i]] < dAEBG[[i]]){cBGAE <- cBGAE+1} + if(dBGAF[[i]] > dAFBG[[i]]){bBGAF <- bBGAF+1} + if(dBGAF[[i]] < dAFBG[[i]]){cBGAF <- cBGAF+1} + if(dBGAG[[i]] > dAGBG[[i]]){bBGAG <- bBGAG+1} + if(dBGAG[[i]] < dAGBG[[i]]){cBGAG <- cBGAG+1} + if(dBGAH[[i]] > dAHBG[[i]]){bBGAH <- bBGAH+1} + if(dBGAH[[i]] < dAHBG[[i]]){cBGAH <- cBGAH+1} + if(dBGBA[[i]] > dBABG[[i]]){bBGBA <- bBGBA+1} + if(dBGBA[[i]] < dBABG[[i]]){cBGBA <- cBGBA+1} + if(dBGBB[[i]] > dBBBG[[i]]){bBGBB <- bBGBB+1} + if(dBGBB[[i]] < dBBBG[[i]]){cBGBB <- cBGBB+1} + if(dBGBC[[i]] > dBCBG[[i]]){bBGBC <- bBGBC+1} + if(dBGBC[[i]] < dBCBG[[i]]){cBGBC <- cBGBC+1} + if(dBGBD[[i]] > dBDBG[[i]]){bBGBD <- bBGBD+1} + if(dBGBD[[i]] < dBDBG[[i]]){cBGBD <- cBGBD+1} + if(dBGBE[[i]] > dBEBG[[i]]){bBGBE <- bBGBE+1} + if(dBGBE[[i]] < dBEBG[[i]]){cBGBE <- cBGBE+1} + if(dBGBF[[i]] > dBFBG[[i]]){bBGBF <- bBGBF+1} + if(dBGBF[[i]] < dBFBG[[i]]){cBGBF <- cBGBF+1} + if(dBGBG[[i]] > dBGBG[[i]]){bBGBG <- bBGBG+1} + if(dBGBG[[i]] < dBGBG[[i]]){cBGBG <- cBGBG+1} + if(dBGBH[[i]] > dBHBG[[i]]){bBGBH <- bBGBH+1} + if(dBGBH[[i]] < dBHBG[[i]]){cBGBH <- cBGBH+1} + if(dBGCA[[i]] > dCABG[[i]]){bBGCA <- bBGCA+1} + if(dBGCA[[i]] < dCABG[[i]]){cBGCA <- cBGCA+1} + if(dBGCB[[i]] > dCBBG[[i]]){bBGCB <- bBGCB+1} + if(dBGCB[[i]] < dCBBG[[i]]){cBGCB <- cBGCB+1} + if(dBGCC[[i]] > dCCBG[[i]]){bBGCC <- bBGCC+1} + if(dBGCC[[i]] < dCCBG[[i]]){cBGCC <- cBGCC+1} + if(dBGCD[[i]] > dCDBG[[i]]){bBGCD <- bBGCD+1} + if(dBGCD[[i]] < dCDBG[[i]]){cBGCD <- cBGCD+1} + if(dBGCE[[i]] > dCEBG[[i]]){bBGCE <- bBGCE+1} + if(dBGCE[[i]] < dCEBG[[i]]){cBGCE <- cBGCE+1} + if(dBGCF[[i]] > dCFBG[[i]]){bBGCF <- bBGCF+1} + if(dBGCF[[i]] < dCFBG[[i]]){cBGCF <- cBGCF+1} + if(dBGCG[[i]] > dCGBG[[i]]){bBGCG <- bBGCG+1} + if(dBGCG[[i]] < dCGBG[[i]]){cBGCG <- cBGCG+1} + if(dBGCH[[i]] > dCHBG[[i]]){bBGCH <- bBGCH+1} + if(dBGCH[[i]] < dCHBG[[i]]){cBGCH <- cBGCH+1} + if(dBHAA[[i]] > dAABH[[i]]){bBHAA <- bBHAA+1} + if(dBHAA[[i]] < dAABH[[i]]){cBHAA <- cBHAA+1} + if(dBHAB[[i]] > dABBH[[i]]){bBHAB <- bBHAB+1} + if(dBHAB[[i]] < dABBH[[i]]){cBHAB <- cBHAB+1} + if(dBHAC[[i]] > dACBH[[i]]){bBHAC <- bBHAC+1} + if(dBHAC[[i]] < dACBH[[i]]){cBHAC <- cBHAC+1} + if(dBHAD[[i]] > dADBH[[i]]){bBHAD <- bBHAD+1} + if(dBHAD[[i]] < dADBH[[i]]){cBHAD <- cBHAD+1} + if(dBHAE[[i]] > dAEBH[[i]]){bBHAE <- bBHAE+1} + if(dBHAE[[i]] < dAEBH[[i]]){cBHAE <- cBHAE+1} + if(dBHAF[[i]] > dAFBH[[i]]){bBHAF <- bBHAF+1} + if(dBHAF[[i]] < dAFBH[[i]]){cBHAF <- cBHAF+1} + if(dBHAG[[i]] > dAGBH[[i]]){bBHAG <- bBHAG+1} + if(dBHAG[[i]] < dAGBH[[i]]){cBHAG <- cBHAG+1} + if(dBHAH[[i]] > dAHBH[[i]]){bBHAH <- bBHAH+1} + if(dBHAH[[i]] < dAHBH[[i]]){cBHAH <- cBHAH+1} + if(dBHBA[[i]] > dBABH[[i]]){bBHBA <- bBHBA+1} + if(dBHBA[[i]] < dBABH[[i]]){cBHBA <- cBHBA+1} + if(dBHBB[[i]] > dBBBH[[i]]){bBHBB <- bBHBB+1} + if(dBHBB[[i]] < dBBBH[[i]]){cBHBB <- cBHBB+1} + if(dBHBC[[i]] > dBCBH[[i]]){bBHBC <- bBHBC+1} + if(dBHBC[[i]] < dBCBH[[i]]){cBHBC <- cBHBC+1} + if(dBHBD[[i]] > dBDBH[[i]]){bBHBD <- bBHBD+1} + if(dBHBD[[i]] < dBDBH[[i]]){cBHBD <- cBHBD+1} + if(dBHBE[[i]] > dBEBH[[i]]){bBHBE <- bBHBE+1} + if(dBHBE[[i]] < dBEBH[[i]]){cBHBE <- cBHBE+1} + if(dBHBF[[i]] > dBFBH[[i]]){bBHBF <- bBHBF+1} + if(dBHBF[[i]] < dBFBH[[i]]){cBHBF <- cBHBF+1} + if(dBHBG[[i]] > dBGBH[[i]]){bBHBG <- bBHBG+1} + if(dBHBG[[i]] < dBGBH[[i]]){cBHBG <- cBHBG+1} + if(dBHBH[[i]] > dBHBH[[i]]){bBHBH <- bBHBH+1} + if(dBHBH[[i]] < dBHBH[[i]]){cBHBH <- cBHBH+1} + if(dBHCA[[i]] > dCABH[[i]]){bBHCA <- bBHCA+1} + if(dBHCA[[i]] < dCABH[[i]]){cBHCA <- cBHCA+1} + if(dBHCB[[i]] > dCBBH[[i]]){bBHCB <- bBHCB+1} + if(dBHCB[[i]] < dCBBH[[i]]){cBHCB <- cBHCB+1} + if(dBHCC[[i]] > dCCBH[[i]]){bBHCC <- bBHCC+1} + if(dBHCC[[i]] < dCCBH[[i]]){cBHCC <- cBHCC+1} + if(dBHCD[[i]] > dCDBH[[i]]){bBHCD <- bBHCD+1} + if(dBHCD[[i]] < dCDBH[[i]]){cBHCD <- cBHCD+1} + if(dBHCE[[i]] > dCEBH[[i]]){bBHCE <- bBHCE+1} + if(dBHCE[[i]] < dCEBH[[i]]){cBHCE <- cBHCE+1} + if(dBHCF[[i]] > dCFBH[[i]]){bBHCF <- bBHCF+1} + if(dBHCF[[i]] < dCFBH[[i]]){cBHCF <- cBHCF+1} + if(dBHCG[[i]] > dCGBH[[i]]){bBHCG <- bBHCG+1} + if(dBHCG[[i]] < dCGBH[[i]]){cBHCG <- cBHCG+1} + if(dBHCH[[i]] > dCHBH[[i]]){bBHCH <- bBHCH+1} + if(dBHCH[[i]] < dCHBH[[i]]){cBHCH <- cBHCH+1} + if(dCAAA[[i]] > dAACA[[i]]){bCAAA <- bCAAA+1} + if(dCAAA[[i]] < dAACA[[i]]){cCAAA <- cCAAA+1} + if(dCAAB[[i]] > dABCA[[i]]){bCAAB <- bCAAB+1} + if(dCAAB[[i]] < dABCA[[i]]){cCAAB <- cCAAB+1} + if(dCAAC[[i]] > dACCA[[i]]){bCAAC <- bCAAC+1} + if(dCAAC[[i]] < dACCA[[i]]){cCAAC <- cCAAC+1} + if(dCAAD[[i]] > dADCA[[i]]){bCAAD <- bCAAD+1} + if(dCAAD[[i]] < dADCA[[i]]){cCAAD <- cCAAD+1} + if(dCAAE[[i]] > dAECA[[i]]){bCAAE <- bCAAE+1} + if(dCAAE[[i]] < dAECA[[i]]){cCAAE <- cCAAE+1} + if(dCAAF[[i]] > dAFCA[[i]]){bCAAF <- bCAAF+1} + if(dCAAF[[i]] < dAFCA[[i]]){cCAAF <- cCAAF+1} + if(dCAAG[[i]] > dAGCA[[i]]){bCAAG <- bCAAG+1} + if(dCAAG[[i]] < dAGCA[[i]]){cCAAG <- cCAAG+1} + if(dCAAH[[i]] > dAHCA[[i]]){bCAAH <- bCAAH+1} + if(dCAAH[[i]] < dAHCA[[i]]){cCAAH <- cCAAH+1} + if(dCABA[[i]] > dBACA[[i]]){bCABA <- bCABA+1} + if(dCABA[[i]] < dBACA[[i]]){cCABA <- cCABA+1} + if(dCABB[[i]] > dBBCA[[i]]){bCABB <- bCABB+1} + if(dCABB[[i]] < dBBCA[[i]]){cCABB <- cCABB+1} + if(dCABC[[i]] > dBCCA[[i]]){bCABC <- bCABC+1} + if(dCABC[[i]] < dBCCA[[i]]){cCABC <- cCABC+1} + if(dCABD[[i]] > dBDCA[[i]]){bCABD <- bCABD+1} + if(dCABD[[i]] < dBDCA[[i]]){cCABD <- cCABD+1} + if(dCABE[[i]] > dBECA[[i]]){bCABE <- bCABE+1} + if(dCABE[[i]] < dBECA[[i]]){cCABE <- cCABE+1} + if(dCABF[[i]] > dBFCA[[i]]){bCABF <- bCABF+1} + if(dCABF[[i]] < dBFCA[[i]]){cCABF <- cCABF+1} + if(dCABG[[i]] > dBGCA[[i]]){bCABG <- bCABG+1} + if(dCABG[[i]] < dBGCA[[i]]){cCABG <- cCABG+1} + if(dCABH[[i]] > dBHCA[[i]]){bCABH <- bCABH+1} + if(dCABH[[i]] < dBHCA[[i]]){cCABH <- cCABH+1} + if(dCACA[[i]] > dCACA[[i]]){bCACA <- bCACA+1} + if(dCACA[[i]] < dCACA[[i]]){cCACA <- cCACA+1} + if(dCACB[[i]] > dCBCA[[i]]){bCACB <- bCACB+1} + if(dCACB[[i]] < dCBCA[[i]]){cCACB <- cCACB+1} + if(dCACC[[i]] > dCCCA[[i]]){bCACC <- bCACC+1} + if(dCACC[[i]] < dCCCA[[i]]){cCACC <- cCACC+1} + if(dCACD[[i]] > dCDCA[[i]]){bCACD <- bCACD+1} + if(dCACD[[i]] < dCDCA[[i]]){cCACD <- cCACD+1} + if(dCACE[[i]] > dCECA[[i]]){bCACE <- bCACE+1} + if(dCACE[[i]] < dCECA[[i]]){cCACE <- cCACE+1} + if(dCACF[[i]] > dCFCA[[i]]){bCACF <- bCACF+1} + if(dCACF[[i]] < dCFCA[[i]]){cCACF <- cCACF+1} + if(dCACG[[i]] > dCGCA[[i]]){bCACG <- bCACG+1} + if(dCACG[[i]] < dCGCA[[i]]){cCACG <- cCACG+1} + if(dCACH[[i]] > dCHCA[[i]]){bCACH <- bCACH+1} + if(dCACH[[i]] < dCHCA[[i]]){cCACH <- cCACH+1} + if(dCBAA[[i]] > dAACB[[i]]){bCBAA <- bCBAA+1} + if(dCBAA[[i]] < dAACB[[i]]){cCBAA <- cCBAA+1} + if(dCBAB[[i]] > dABCB[[i]]){bCBAB <- bCBAB+1} + if(dCBAB[[i]] < dABCB[[i]]){cCBAB <- cCBAB+1} + if(dCBAC[[i]] > dACCB[[i]]){bCBAC <- bCBAC+1} + if(dCBAC[[i]] < dACCB[[i]]){cCBAC <- cCBAC+1} + if(dCBAD[[i]] > dADCB[[i]]){bCBAD <- bCBAD+1} + if(dCBAD[[i]] < dADCB[[i]]){cCBAD <- cCBAD+1} + if(dCBAE[[i]] > dAECB[[i]]){bCBAE <- bCBAE+1} + if(dCBAE[[i]] < dAECB[[i]]){cCBAE <- cCBAE+1} + if(dCBAF[[i]] > dAFCB[[i]]){bCBAF <- bCBAF+1} + if(dCBAF[[i]] < dAFCB[[i]]){cCBAF <- cCBAF+1} + if(dCBAG[[i]] > dAGCB[[i]]){bCBAG <- bCBAG+1} + if(dCBAG[[i]] < dAGCB[[i]]){cCBAG <- cCBAG+1} + if(dCBAH[[i]] > dAHCB[[i]]){bCBAH <- bCBAH+1} + if(dCBAH[[i]] < dAHCB[[i]]){cCBAH <- cCBAH+1} + if(dCBBA[[i]] > dBACB[[i]]){bCBBA <- bCBBA+1} + if(dCBBA[[i]] < dBACB[[i]]){cCBBA <- cCBBA+1} + if(dCBBB[[i]] > dBBCB[[i]]){bCBBB <- bCBBB+1} + if(dCBBB[[i]] < dBBCB[[i]]){cCBBB <- cCBBB+1} + if(dCBBC[[i]] > dBCCB[[i]]){bCBBC <- bCBBC+1} + if(dCBBC[[i]] < dBCCB[[i]]){cCBBC <- cCBBC+1} + if(dCBBD[[i]] > dBDCB[[i]]){bCBBD <- bCBBD+1} + if(dCBBD[[i]] < dBDCB[[i]]){cCBBD <- cCBBD+1} + if(dCBBE[[i]] > dBECB[[i]]){bCBBE <- bCBBE+1} + if(dCBBE[[i]] < dBECB[[i]]){cCBBE <- cCBBE+1} + if(dCBBF[[i]] > dBFCB[[i]]){bCBBF <- bCBBF+1} + if(dCBBF[[i]] < dBFCB[[i]]){cCBBF <- cCBBF+1} + if(dCBBG[[i]] > dBGCB[[i]]){bCBBG <- bCBBG+1} + if(dCBBG[[i]] < dBGCB[[i]]){cCBBG <- cCBBG+1} + if(dCBBH[[i]] > dBHCB[[i]]){bCBBH <- bCBBH+1} + if(dCBBH[[i]] < dBHCB[[i]]){cCBBH <- cCBBH+1} + if(dCBCA[[i]] > dCACB[[i]]){bCBCA <- bCBCA+1} + if(dCBCA[[i]] < dCACB[[i]]){cCBCA <- cCBCA+1} + if(dCBCB[[i]] > dCBCB[[i]]){bCBCB <- bCBCB+1} + if(dCBCB[[i]] < dCBCB[[i]]){cCBCB <- cCBCB+1} + if(dCBCC[[i]] > dCCCB[[i]]){bCBCC <- bCBCC+1} + if(dCBCC[[i]] < dCCCB[[i]]){cCBCC <- cCBCC+1} + if(dCBCD[[i]] > dCDCB[[i]]){bCBCD <- bCBCD+1} + if(dCBCD[[i]] < dCDCB[[i]]){cCBCD <- cCBCD+1} + if(dCBCE[[i]] > dCECB[[i]]){bCBCE <- bCBCE+1} + if(dCBCE[[i]] < dCECB[[i]]){cCBCE <- cCBCE+1} + if(dCBCF[[i]] > dCFCB[[i]]){bCBCF <- bCBCF+1} + if(dCBCF[[i]] < dCFCB[[i]]){cCBCF <- cCBCF+1} + if(dCBCG[[i]] > dCGCB[[i]]){bCBCG <- bCBCG+1} + if(dCBCG[[i]] < dCGCB[[i]]){cCBCG <- cCBCG+1} + if(dCBCH[[i]] > dCHCB[[i]]){bCBCH <- bCBCH+1} + if(dCBCH[[i]] < dCHCB[[i]]){cCBCH <- cCBCH+1} + if(dCCAA[[i]] > dAACC[[i]]){bCCAA <- bCCAA+1} + if(dCCAA[[i]] < dAACC[[i]]){cCCAA <- cCCAA+1} + if(dCCAB[[i]] > dABCC[[i]]){bCCAB <- bCCAB+1} + if(dCCAB[[i]] < dABCC[[i]]){cCCAB <- cCCAB+1} + if(dCCAC[[i]] > dACCC[[i]]){bCCAC <- bCCAC+1} + if(dCCAC[[i]] < dACCC[[i]]){cCCAC <- cCCAC+1} + if(dCCAD[[i]] > dADCC[[i]]){bCCAD <- bCCAD+1} + if(dCCAD[[i]] < dADCC[[i]]){cCCAD <- cCCAD+1} + if(dCCAE[[i]] > dAECC[[i]]){bCCAE <- bCCAE+1} + if(dCCAE[[i]] < dAECC[[i]]){cCCAE <- cCCAE+1} + if(dCCAF[[i]] > dAFCC[[i]]){bCCAF <- bCCAF+1} + if(dCCAF[[i]] < dAFCC[[i]]){cCCAF <- cCCAF+1} + if(dCCAG[[i]] > dAGCC[[i]]){bCCAG <- bCCAG+1} + if(dCCAG[[i]] < dAGCC[[i]]){cCCAG <- cCCAG+1} + if(dCCAH[[i]] > dAHCC[[i]]){bCCAH <- bCCAH+1} + if(dCCAH[[i]] < dAHCC[[i]]){cCCAH <- cCCAH+1} + if(dCCBA[[i]] > dBACC[[i]]){bCCBA <- bCCBA+1} + if(dCCBA[[i]] < dBACC[[i]]){cCCBA <- cCCBA+1} + if(dCCBB[[i]] > dBBCC[[i]]){bCCBB <- bCCBB+1} + if(dCCBB[[i]] < dBBCC[[i]]){cCCBB <- cCCBB+1} + if(dCCBC[[i]] > dBCCC[[i]]){bCCBC <- bCCBC+1} + if(dCCBC[[i]] < dBCCC[[i]]){cCCBC <- cCCBC+1} + if(dCCBD[[i]] > dBDCC[[i]]){bCCBD <- bCCBD+1} + if(dCCBD[[i]] < dBDCC[[i]]){cCCBD <- cCCBD+1} + if(dCCBE[[i]] > dBECC[[i]]){bCCBE <- bCCBE+1} + if(dCCBE[[i]] < dBECC[[i]]){cCCBE <- cCCBE+1} + if(dCCBF[[i]] > dBFCC[[i]]){bCCBF <- bCCBF+1} + if(dCCBF[[i]] < dBFCC[[i]]){cCCBF <- cCCBF+1} + if(dCCBG[[i]] > dBGCC[[i]]){bCCBG <- bCCBG+1} + if(dCCBG[[i]] < dBGCC[[i]]){cCCBG <- cCCBG+1} + if(dCCBH[[i]] > dBHCC[[i]]){bCCBH <- bCCBH+1} + if(dCCBH[[i]] < dBHCC[[i]]){cCCBH <- cCCBH+1} + if(dCCCA[[i]] > dCACC[[i]]){bCCCA <- bCCCA+1} + if(dCCCA[[i]] < dCACC[[i]]){cCCCA <- cCCCA+1} + if(dCCCB[[i]] > dCBCC[[i]]){bCCCB <- bCCCB+1} + if(dCCCB[[i]] < dCBCC[[i]]){cCCCB <- cCCCB+1} + if(dCCCC[[i]] > dCCCC[[i]]){bCCCC <- bCCCC+1} + if(dCCCC[[i]] < dCCCC[[i]]){cCCCC <- cCCCC+1} + if(dCCCD[[i]] > dCDCC[[i]]){bCCCD <- bCCCD+1} + if(dCCCD[[i]] < dCDCC[[i]]){cCCCD <- cCCCD+1} + if(dCCCE[[i]] > dCECC[[i]]){bCCCE <- bCCCE+1} + if(dCCCE[[i]] < dCECC[[i]]){cCCCE <- cCCCE+1} + if(dCCCF[[i]] > dCFCC[[i]]){bCCCF <- bCCCF+1} + if(dCCCF[[i]] < dCFCC[[i]]){cCCCF <- cCCCF+1} + if(dCCCG[[i]] > dCGCC[[i]]){bCCCG <- bCCCG+1} + if(dCCCG[[i]] < dCGCC[[i]]){cCCCG <- cCCCG+1} + if(dCCCH[[i]] > dCHCC[[i]]){bCCCH <- bCCCH+1} + if(dCCCH[[i]] < dCHCC[[i]]){cCCCH <- cCCCH+1} + if(dCDAA[[i]] > dAACD[[i]]){bCDAA <- bCDAA+1} + if(dCDAA[[i]] < dAACD[[i]]){cCDAA <- cCDAA+1} + if(dCDAB[[i]] > dABCD[[i]]){bCDAB <- bCDAB+1} + if(dCDAB[[i]] < dABCD[[i]]){cCDAB <- cCDAB+1} + if(dCDAC[[i]] > dACCD[[i]]){bCDAC <- bCDAC+1} + if(dCDAC[[i]] < dACCD[[i]]){cCDAC <- cCDAC+1} + if(dCDAD[[i]] > dADCD[[i]]){bCDAD <- bCDAD+1} + if(dCDAD[[i]] < dADCD[[i]]){cCDAD <- cCDAD+1} + if(dCDAE[[i]] > dAECD[[i]]){bCDAE <- bCDAE+1} + if(dCDAE[[i]] < dAECD[[i]]){cCDAE <- cCDAE+1} + if(dCDAF[[i]] > dAFCD[[i]]){bCDAF <- bCDAF+1} + if(dCDAF[[i]] < dAFCD[[i]]){cCDAF <- cCDAF+1} + if(dCDAG[[i]] > dAGCD[[i]]){bCDAG <- bCDAG+1} + if(dCDAG[[i]] < dAGCD[[i]]){cCDAG <- cCDAG+1} + if(dCDAH[[i]] > dAHCD[[i]]){bCDAH <- bCDAH+1} + if(dCDAH[[i]] < dAHCD[[i]]){cCDAH <- cCDAH+1} + if(dCDBA[[i]] > dBACD[[i]]){bCDBA <- bCDBA+1} + if(dCDBA[[i]] < dBACD[[i]]){cCDBA <- cCDBA+1} + if(dCDBB[[i]] > dBBCD[[i]]){bCDBB <- bCDBB+1} + if(dCDBB[[i]] < dBBCD[[i]]){cCDBB <- cCDBB+1} + if(dCDBC[[i]] > dBCCD[[i]]){bCDBC <- bCDBC+1} + if(dCDBC[[i]] < dBCCD[[i]]){cCDBC <- cCDBC+1} + if(dCDBD[[i]] > dBDCD[[i]]){bCDBD <- bCDBD+1} + if(dCDBD[[i]] < dBDCD[[i]]){cCDBD <- cCDBD+1} + if(dCDBE[[i]] > dBECD[[i]]){bCDBE <- bCDBE+1} + if(dCDBE[[i]] < dBECD[[i]]){cCDBE <- cCDBE+1} + if(dCDBF[[i]] > dBFCD[[i]]){bCDBF <- bCDBF+1} + if(dCDBF[[i]] < dBFCD[[i]]){cCDBF <- cCDBF+1} + if(dCDBG[[i]] > dBGCD[[i]]){bCDBG <- bCDBG+1} + if(dCDBG[[i]] < dBGCD[[i]]){cCDBG <- cCDBG+1} + if(dCDBH[[i]] > dBHCD[[i]]){bCDBH <- bCDBH+1} + if(dCDBH[[i]] < dBHCD[[i]]){cCDBH <- cCDBH+1} + if(dCDCA[[i]] > dCACD[[i]]){bCDCA <- bCDCA+1} + if(dCDCA[[i]] < dCACD[[i]]){cCDCA <- cCDCA+1} + if(dCDCB[[i]] > dCBCD[[i]]){bCDCB <- bCDCB+1} + if(dCDCB[[i]] < dCBCD[[i]]){cCDCB <- cCDCB+1} + if(dCDCC[[i]] > dCCCD[[i]]){bCDCC <- bCDCC+1} + if(dCDCC[[i]] < dCCCD[[i]]){cCDCC <- cCDCC+1} + if(dCDCD[[i]] > dCDCD[[i]]){bCDCD <- bCDCD+1} + if(dCDCD[[i]] < dCDCD[[i]]){cCDCD <- cCDCD+1} + if(dCDCE[[i]] > dCECD[[i]]){bCDCE <- bCDCE+1} + if(dCDCE[[i]] < dCECD[[i]]){cCDCE <- cCDCE+1} + if(dCDCF[[i]] > dCFCD[[i]]){bCDCF <- bCDCF+1} + if(dCDCF[[i]] < dCFCD[[i]]){cCDCF <- cCDCF+1} + if(dCDCG[[i]] > dCGCD[[i]]){bCDCG <- bCDCG+1} + if(dCDCG[[i]] < dCGCD[[i]]){cCDCG <- cCDCG+1} + if(dCDCH[[i]] > dCHCD[[i]]){bCDCH <- bCDCH+1} + if(dCDCH[[i]] < dCHCD[[i]]){cCDCH <- cCDCH+1} + if(dCEAA[[i]] > dAACE[[i]]){bCEAA <- bCEAA+1} + if(dCEAA[[i]] < dAACE[[i]]){cCEAA <- cCEAA+1} + if(dCEAB[[i]] > dABCE[[i]]){bCEAB <- bCEAB+1} + if(dCEAB[[i]] < dABCE[[i]]){cCEAB <- cCEAB+1} + if(dCEAC[[i]] > dACCE[[i]]){bCEAC <- bCEAC+1} + if(dCEAC[[i]] < dACCE[[i]]){cCEAC <- cCEAC+1} + if(dCEAD[[i]] > dADCE[[i]]){bCEAD <- bCEAD+1} + if(dCEAD[[i]] < dADCE[[i]]){cCEAD <- cCEAD+1} + if(dCEAE[[i]] > dAECE[[i]]){bCEAE <- bCEAE+1} + if(dCEAE[[i]] < dAECE[[i]]){cCEAE <- cCEAE+1} + if(dCEAF[[i]] > dAFCE[[i]]){bCEAF <- bCEAF+1} + if(dCEAF[[i]] < dAFCE[[i]]){cCEAF <- cCEAF+1} + if(dCEAG[[i]] > dAGCE[[i]]){bCEAG <- bCEAG+1} + if(dCEAG[[i]] < dAGCE[[i]]){cCEAG <- cCEAG+1} + if(dCEAH[[i]] > dAHCE[[i]]){bCEAH <- bCEAH+1} + if(dCEAH[[i]] < dAHCE[[i]]){cCEAH <- cCEAH+1} + if(dCEBA[[i]] > dBACE[[i]]){bCEBA <- bCEBA+1} + if(dCEBA[[i]] < dBACE[[i]]){cCEBA <- cCEBA+1} + if(dCEBB[[i]] > dBBCE[[i]]){bCEBB <- bCEBB+1} + if(dCEBB[[i]] < dBBCE[[i]]){cCEBB <- cCEBB+1} + if(dCEBC[[i]] > dBCCE[[i]]){bCEBC <- bCEBC+1} + if(dCEBC[[i]] < dBCCE[[i]]){cCEBC <- cCEBC+1} + if(dCEBD[[i]] > dBDCE[[i]]){bCEBD <- bCEBD+1} + if(dCEBD[[i]] < dBDCE[[i]]){cCEBD <- cCEBD+1} + if(dCEBE[[i]] > dBECE[[i]]){bCEBE <- bCEBE+1} + if(dCEBE[[i]] < dBECE[[i]]){cCEBE <- cCEBE+1} + if(dCEBF[[i]] > dBFCE[[i]]){bCEBF <- bCEBF+1} + if(dCEBF[[i]] < dBFCE[[i]]){cCEBF <- cCEBF+1} + if(dCEBG[[i]] > dBGCE[[i]]){bCEBG <- bCEBG+1} + if(dCEBG[[i]] < dBGCE[[i]]){cCEBG <- cCEBG+1} + if(dCEBH[[i]] > dBHCE[[i]]){bCEBH <- bCEBH+1} + if(dCEBH[[i]] < dBHCE[[i]]){cCEBH <- cCEBH+1} + if(dCECA[[i]] > dCACE[[i]]){bCECA <- bCECA+1} + if(dCECA[[i]] < dCACE[[i]]){cCECA <- cCECA+1} + if(dCECB[[i]] > dCBCE[[i]]){bCECB <- bCECB+1} + if(dCECB[[i]] < dCBCE[[i]]){cCECB <- cCECB+1} + if(dCECC[[i]] > dCCCE[[i]]){bCECC <- bCECC+1} + if(dCECC[[i]] < dCCCE[[i]]){cCECC <- cCECC+1} + if(dCECD[[i]] > dCDCE[[i]]){bCECD <- bCECD+1} + if(dCECD[[i]] < dCDCE[[i]]){cCECD <- cCECD+1} + if(dCECE[[i]] > dCECE[[i]]){bCECE <- bCECE+1} + if(dCECE[[i]] < dCECE[[i]]){cCECE <- cCECE+1} + if(dCECF[[i]] > dCFCE[[i]]){bCECF <- bCECF+1} + if(dCECF[[i]] < dCFCE[[i]]){cCECF <- cCECF+1} + if(dCECG[[i]] > dCGCE[[i]]){bCECG <- bCECG+1} + if(dCECG[[i]] < dCGCE[[i]]){cCECG <- cCECG+1} + if(dCECH[[i]] > dCHCE[[i]]){bCECH <- bCECH+1} + if(dCECH[[i]] < dCHCE[[i]]){cCECH <- cCECH+1} + if(dCFAA[[i]] > dAACF[[i]]){bCFAA <- bCFAA+1} + if(dCFAA[[i]] < dAACF[[i]]){cCFAA <- cCFAA+1} + if(dCFAB[[i]] > dABCF[[i]]){bCFAB <- bCFAB+1} + if(dCFAB[[i]] < dABCF[[i]]){cCFAB <- cCFAB+1} + if(dCFAC[[i]] > dACCF[[i]]){bCFAC <- bCFAC+1} + if(dCFAC[[i]] < dACCF[[i]]){cCFAC <- cCFAC+1} + if(dCFAD[[i]] > dADCF[[i]]){bCFAD <- bCFAD+1} + if(dCFAD[[i]] < dADCF[[i]]){cCFAD <- cCFAD+1} + if(dCFAE[[i]] > dAECF[[i]]){bCFAE <- bCFAE+1} + if(dCFAE[[i]] < dAECF[[i]]){cCFAE <- cCFAE+1} + if(dCFAF[[i]] > dAFCF[[i]]){bCFAF <- bCFAF+1} + if(dCFAF[[i]] < dAFCF[[i]]){cCFAF <- cCFAF+1} + if(dCFAG[[i]] > dAGCF[[i]]){bCFAG <- bCFAG+1} + if(dCFAG[[i]] < dAGCF[[i]]){cCFAG <- cCFAG+1} + if(dCFAH[[i]] > dAHCF[[i]]){bCFAH <- bCFAH+1} + if(dCFAH[[i]] < dAHCF[[i]]){cCFAH <- cCFAH+1} + if(dCFBA[[i]] > dBACF[[i]]){bCFBA <- bCFBA+1} + if(dCFBA[[i]] < dBACF[[i]]){cCFBA <- cCFBA+1} + if(dCFBB[[i]] > dBBCF[[i]]){bCFBB <- bCFBB+1} + if(dCFBB[[i]] < dBBCF[[i]]){cCFBB <- cCFBB+1} + if(dCFBC[[i]] > dBCCF[[i]]){bCFBC <- bCFBC+1} + if(dCFBC[[i]] < dBCCF[[i]]){cCFBC <- cCFBC+1} + if(dCFBD[[i]] > dBDCF[[i]]){bCFBD <- bCFBD+1} + if(dCFBD[[i]] < dBDCF[[i]]){cCFBD <- cCFBD+1} + if(dCFBE[[i]] > dBECF[[i]]){bCFBE <- bCFBE+1} + if(dCFBE[[i]] < dBECF[[i]]){cCFBE <- cCFBE+1} + if(dCFBF[[i]] > dBFCF[[i]]){bCFBF <- bCFBF+1} + if(dCFBF[[i]] < dBFCF[[i]]){cCFBF <- cCFBF+1} + if(dCFBG[[i]] > dBGCF[[i]]){bCFBG <- bCFBG+1} + if(dCFBG[[i]] < dBGCF[[i]]){cCFBG <- cCFBG+1} + if(dCFBH[[i]] > dBHCF[[i]]){bCFBH <- bCFBH+1} + if(dCFBH[[i]] < dBHCF[[i]]){cCFBH <- cCFBH+1} + if(dCFCA[[i]] > dCACF[[i]]){bCFCA <- bCFCA+1} + if(dCFCA[[i]] < dCACF[[i]]){cCFCA <- cCFCA+1} + if(dCFCB[[i]] > dCBCF[[i]]){bCFCB <- bCFCB+1} + if(dCFCB[[i]] < dCBCF[[i]]){cCFCB <- cCFCB+1} + if(dCFCC[[i]] > dCCCF[[i]]){bCFCC <- bCFCC+1} + if(dCFCC[[i]] < dCCCF[[i]]){cCFCC <- cCFCC+1} + if(dCFCD[[i]] > dCDCF[[i]]){bCFCD <- bCFCD+1} + if(dCFCD[[i]] < dCDCF[[i]]){cCFCD <- cCFCD+1} + if(dCFCE[[i]] > dCECF[[i]]){bCFCE <- bCFCE+1} + if(dCFCE[[i]] < dCECF[[i]]){cCFCE <- cCFCE+1} + if(dCFCF[[i]] > dCFCF[[i]]){bCFCF <- bCFCF+1} + if(dCFCF[[i]] < dCFCF[[i]]){cCFCF <- cCFCF+1} + if(dCFCG[[i]] > dCGCF[[i]]){bCFCG <- bCFCG+1} + if(dCFCG[[i]] < dCGCF[[i]]){cCFCG <- cCFCG+1} + if(dCFCH[[i]] > dCHCF[[i]]){bCFCH <- bCFCH+1} + if(dCFCH[[i]] < dCHCF[[i]]){cCFCH <- cCFCH+1} + if(dCGAA[[i]] > dAACG[[i]]){bCGAA <- bCGAA+1} + if(dCGAA[[i]] < dAACG[[i]]){cCGAA <- cCGAA+1} + if(dCGAB[[i]] > dABCG[[i]]){bCGAB <- bCGAB+1} + if(dCGAB[[i]] < dABCG[[i]]){cCGAB <- cCGAB+1} + if(dCGAC[[i]] > dACCG[[i]]){bCGAC <- bCGAC+1} + if(dCGAC[[i]] < dACCG[[i]]){cCGAC <- cCGAC+1} + if(dCGAD[[i]] > dADCG[[i]]){bCGAD <- bCGAD+1} + if(dCGAD[[i]] < dADCG[[i]]){cCGAD <- cCGAD+1} + if(dCGAE[[i]] > dAECG[[i]]){bCGAE <- bCGAE+1} + if(dCGAE[[i]] < dAECG[[i]]){cCGAE <- cCGAE+1} + if(dCGAF[[i]] > dAFCG[[i]]){bCGAF <- bCGAF+1} + if(dCGAF[[i]] < dAFCG[[i]]){cCGAF <- cCGAF+1} + if(dCGAG[[i]] > dAGCG[[i]]){bCGAG <- bCGAG+1} + if(dCGAG[[i]] < dAGCG[[i]]){cCGAG <- cCGAG+1} + if(dCGAH[[i]] > dAHCG[[i]]){bCGAH <- bCGAH+1} + if(dCGAH[[i]] < dAHCG[[i]]){cCGAH <- cCGAH+1} + if(dCGBA[[i]] > dBACG[[i]]){bCGBA <- bCGBA+1} + if(dCGBA[[i]] < dBACG[[i]]){cCGBA <- cCGBA+1} + if(dCGBB[[i]] > dBBCG[[i]]){bCGBB <- bCGBB+1} + if(dCGBB[[i]] < dBBCG[[i]]){cCGBB <- cCGBB+1} + if(dCGBC[[i]] > dBCCG[[i]]){bCGBC <- bCGBC+1} + if(dCGBC[[i]] < dBCCG[[i]]){cCGBC <- cCGBC+1} + if(dCGBD[[i]] > dBDCG[[i]]){bCGBD <- bCGBD+1} + if(dCGBD[[i]] < dBDCG[[i]]){cCGBD <- cCGBD+1} + if(dCGBE[[i]] > dBECG[[i]]){bCGBE <- bCGBE+1} + if(dCGBE[[i]] < dBECG[[i]]){cCGBE <- cCGBE+1} + if(dCGBF[[i]] > dBFCG[[i]]){bCGBF <- bCGBF+1} + if(dCGBF[[i]] < dBFCG[[i]]){cCGBF <- cCGBF+1} + if(dCGBG[[i]] > dBGCG[[i]]){bCGBG <- bCGBG+1} + if(dCGBG[[i]] < dBGCG[[i]]){cCGBG <- cCGBG+1} + if(dCGBH[[i]] > dBHCG[[i]]){bCGBH <- bCGBH+1} + if(dCGBH[[i]] < dBHCG[[i]]){cCGBH <- cCGBH+1} + if(dCGCA[[i]] > dCACG[[i]]){bCGCA <- bCGCA+1} + if(dCGCA[[i]] < dCACG[[i]]){cCGCA <- cCGCA+1} + if(dCGCB[[i]] > dCBCG[[i]]){bCGCB <- bCGCB+1} + if(dCGCB[[i]] < dCBCG[[i]]){cCGCB <- cCGCB+1} + if(dCGCC[[i]] > dCCCG[[i]]){bCGCC <- bCGCC+1} + if(dCGCC[[i]] < dCCCG[[i]]){cCGCC <- cCGCC+1} + if(dCGCD[[i]] > dCDCG[[i]]){bCGCD <- bCGCD+1} + if(dCGCD[[i]] < dCDCG[[i]]){cCGCD <- cCGCD+1} + if(dCGCE[[i]] > dCECG[[i]]){bCGCE <- bCGCE+1} + if(dCGCE[[i]] < dCECG[[i]]){cCGCE <- cCGCE+1} + if(dCGCF[[i]] > dCFCG[[i]]){bCGCF <- bCGCF+1} + if(dCGCF[[i]] < dCFCG[[i]]){cCGCF <- cCGCF+1} + if(dCGCG[[i]] > dCGCG[[i]]){bCGCG <- bCGCG+1} + if(dCGCG[[i]] < dCGCG[[i]]){cCGCG <- cCGCG+1} + if(dCGCH[[i]] > dCHCG[[i]]){bCGCH <- bCGCH+1} + if(dCGCH[[i]] < dCHCG[[i]]){cCGCH <- cCGCH+1} + if(dCHAA[[i]] > dAACH[[i]]){bCHAA <- bCHAA+1} + if(dCHAA[[i]] < dAACH[[i]]){cCHAA <- cCHAA+1} + if(dCHAB[[i]] > dABCH[[i]]){bCHAB <- bCHAB+1} + if(dCHAB[[i]] < dABCH[[i]]){cCHAB <- cCHAB+1} + if(dCHAC[[i]] > dACCH[[i]]){bCHAC <- bCHAC+1} + if(dCHAC[[i]] < dACCH[[i]]){cCHAC <- cCHAC+1} + if(dCHAD[[i]] > dADCH[[i]]){bCHAD <- bCHAD+1} + if(dCHAD[[i]] < dADCH[[i]]){cCHAD <- cCHAD+1} + if(dCHAE[[i]] > dAECH[[i]]){bCHAE <- bCHAE+1} + if(dCHAE[[i]] < dAECH[[i]]){cCHAE <- cCHAE+1} + if(dCHAF[[i]] > dAFCH[[i]]){bCHAF <- bCHAF+1} + if(dCHAF[[i]] < dAFCH[[i]]){cCHAF <- cCHAF+1} + if(dCHAG[[i]] > dAGCH[[i]]){bCHAG <- bCHAG+1} + if(dCHAG[[i]] < dAGCH[[i]]){cCHAG <- cCHAG+1} + if(dCHAH[[i]] > dAHCH[[i]]){bCHAH <- bCHAH+1} + if(dCHAH[[i]] < dAHCH[[i]]){cCHAH <- cCHAH+1} + if(dCHBA[[i]] > dBACH[[i]]){bCHBA <- bCHBA+1} + if(dCHBA[[i]] < dBACH[[i]]){cCHBA <- cCHBA+1} + if(dCHBB[[i]] > dBBCH[[i]]){bCHBB <- bCHBB+1} + if(dCHBB[[i]] < dBBCH[[i]]){cCHBB <- cCHBB+1} + if(dCHBC[[i]] > dBCCH[[i]]){bCHBC <- bCHBC+1} + if(dCHBC[[i]] < dBCCH[[i]]){cCHBC <- cCHBC+1} + if(dCHBD[[i]] > dBDCH[[i]]){bCHBD <- bCHBD+1} + if(dCHBD[[i]] < dBDCH[[i]]){cCHBD <- cCHBD+1} + if(dCHBE[[i]] > dBECH[[i]]){bCHBE <- bCHBE+1} + if(dCHBE[[i]] < dBECH[[i]]){cCHBE <- cCHBE+1} + if(dCHBF[[i]] > dBFCH[[i]]){bCHBF <- bCHBF+1} + if(dCHBF[[i]] < dBFCH[[i]]){cCHBF <- cCHBF+1} + if(dCHBG[[i]] > dBGCH[[i]]){bCHBG <- bCHBG+1} + if(dCHBG[[i]] < dBGCH[[i]]){cCHBG <- cCHBG+1} + if(dCHBH[[i]] > dBHCH[[i]]){bCHBH <- bCHBH+1} + if(dCHBH[[i]] < dBHCH[[i]]){cCHBH <- cCHBH+1} + if(dCHCA[[i]] > dCACH[[i]]){bCHCA <- bCHCA+1} + if(dCHCA[[i]] < dCACH[[i]]){cCHCA <- cCHCA+1} + if(dCHCB[[i]] > dCBCH[[i]]){bCHCB <- bCHCB+1} + if(dCHCB[[i]] < dCBCH[[i]]){cCHCB <- cCHCB+1} + if(dCHCC[[i]] > dCCCH[[i]]){bCHCC <- bCHCC+1} + if(dCHCC[[i]] < dCCCH[[i]]){cCHCC <- cCHCC+1} + if(dCHCD[[i]] > dCDCH[[i]]){bCHCD <- bCHCD+1} + if(dCHCD[[i]] < dCDCH[[i]]){cCHCD <- cCHCD+1} + if(dCHCE[[i]] > dCECH[[i]]){bCHCE <- bCHCE+1} + if(dCHCE[[i]] < dCECH[[i]]){cCHCE <- cCHCE+1} + if(dCHCF[[i]] > dCFCH[[i]]){bCHCF <- bCHCF+1} + if(dCHCF[[i]] < dCFCH[[i]]){cCHCF <- cCHCF+1} + if(dCHCG[[i]] > dCGCH[[i]]){bCHCG <- bCHCG+1} + if(dCHCG[[i]] < dCGCH[[i]]){cCHCG <- cCHCG+1} + if(dCHCH[[i]] > dCHCH[[i]]){bCHCH <- bCHCH+1} + if(dCHCH[[i]] < dCHCH[[i]]){cCHCH <- cCHCH+1} + } > > > MNAAAA <- ((bAAAA - cAAAA)^2 / (bAAAA + cAAAA)) > MNAAAA [1] NaN > dchisq(MNAAAA, df=1) [1] NaN > MNAAAB <- ((bAAAB - cAAAB)^2 / (bAAAB + cAAAB)) > MNAAAB [1] 8 > dchisq(MNAAAB, df=1) [1] 0.002583373 > MNAAAC <- ((bAAAC - cAAAC)^2 / (bAAAC + cAAAC)) > MNAAAC [1] 9 > dchisq(MNAAAC, df=1) [1] 0.001477283 > MNAAAD <- ((bAAAD - cAAAD)^2 / (bAAAD + cAAAD)) > MNAAAD [1] 10 > dchisq(MNAAAD, df=1) [1] 0.0008500367 > MNAAAE <- ((bAAAE - cAAAE)^2 / (bAAAE + cAAAE)) > MNAAAE [1] 10 > dchisq(MNAAAE, df=1) [1] 0.0008500367 > MNAAAF <- ((bAAAF - cAAAF)^2 / (bAAAF + cAAAF)) > MNAAAF [1] 2 > dchisq(MNAAAF, df=1) [1] 0.1037769 > MNAAAG <- ((bAAAG - cAAAG)^2 / (bAAAG + cAAAG)) > MNAAAG [1] 5 > dchisq(MNAAAG, df=1) [1] 0.01464498 > MNAAAH <- ((bAAAH - cAAAH)^2 / (bAAAH + cAAAH)) > MNAAAH [1] 8 > dchisq(MNAAAH, df=1) [1] 0.002583373 > MNAABA <- ((bAABA - cAABA)^2 / (bAABA + cAABA)) > MNAABA [1] 3 > dchisq(MNAABA, df=1) [1] 0.05139344 > MNAABB <- ((bAABB - cAABB)^2 / (bAABB + cAABB)) > MNAABB [1] 9 > dchisq(MNAABB, df=1) [1] 0.001477283 > MNAABC <- ((bAABC - cAABC)^2 / (bAABC + cAABC)) > MNAABC [1] 10 > dchisq(MNAABC, df=1) [1] 0.0008500367 > MNAABD <- ((bAABD - cAABD)^2 / (bAABD + cAABD)) > MNAABD [1] 10 > dchisq(MNAABD, df=1) [1] 0.0008500367 > MNAABE <- ((bAABE - cAABE)^2 / (bAABE + cAABE)) > MNAABE [1] 10 > dchisq(MNAABE, df=1) [1] 0.0008500367 > MNAABF <- ((bAABF - cAABF)^2 / (bAABF + cAABF)) > MNAABF [1] 9 > dchisq(MNAABF, df=1) [1] 0.001477283 > MNAABG <- ((bAABG - cAABG)^2 / (bAABG + cAABG)) > MNAABG [1] 10 > dchisq(MNAABG, df=1) [1] 0.0008500367 > MNAABH <- ((bAABH - cAABH)^2 / (bAABH + cAABH)) > MNAABH [1] 9 > dchisq(MNAABH, df=1) [1] 0.001477283 > MNAACA <- ((bAACA - cAACA)^2 / (bAACA + cAACA)) > MNAACA [1] 1 > dchisq(MNAACA, df=1) [1] 0.2419707 > MNAACB <- ((bAACB - cAACB)^2 / (bAACB + cAACB)) > MNAACB [1] 7 > dchisq(MNAACB, df=1) [1] 0.004553343 > MNAACC <- ((bAACC - cAACC)^2 / (bAACC + cAACC)) > MNAACC [1] 8 > dchisq(MNAACC, df=1) [1] 0.002583373 > MNAACD <- ((bAACD - cAACD)^2 / (bAACD + cAACD)) > MNAACD [1] 10 > dchisq(MNAACD, df=1) [1] 0.0008500367 > MNAACE <- ((bAACE - cAACE)^2 / (bAACE + cAACE)) > MNAACE [1] 10 > dchisq(MNAACE, df=1) [1] 0.0008500367 > MNAACF <- ((bAACF - cAACF)^2 / (bAACF + cAACF)) > MNAACF [1] 5 > dchisq(MNAACF, df=1) [1] 0.01464498 > MNAACG <- ((bAACG - cAACG)^2 / (bAACG + cAACG)) > MNAACG [1] 7 > dchisq(MNAACG, df=1) [1] 0.004553343 > MNAACH <- ((bAACH - cAACH)^2 / (bAACH + cAACH)) > MNAACH [1] 0 > dchisq(MNAACH, df=1) [1] Inf > MNABAA <- ((bABAA - cABAA)^2 / (bABAA + cABAA)) > MNABAA [1] 8 > dchisq(MNABAA, df=1) [1] 0.002583373 > MNABAB <- ((bABAB - cABAB)^2 / (bABAB + cABAB)) > MNABAB [1] NaN > dchisq(MNABAB, df=1) [1] NaN > MNABAC <- ((bABAC - cABAC)^2 / (bABAC + cABAC)) > MNABAC [1] 2.666667 > dchisq(MNABAC, df=1) [1] 0.06439711 > MNABAD <- ((bABAD - cABAD)^2 / (bABAD + cABAD)) > MNABAD [1] 10 > dchisq(MNABAD, df=1) [1] 0.0008500367 > MNABAE <- ((bABAE - cABAE)^2 / (bABAE + cABAE)) > MNABAE [1] 10 > dchisq(MNABAE, df=1) [1] 0.0008500367 > MNABAF <- ((bABAF - cABAF)^2 / (bABAF + cABAF)) > MNABAF [1] 8 > dchisq(MNABAF, df=1) [1] 0.002583373 > MNABAG <- ((bABAG - cABAG)^2 / (bABAG + cABAG)) > MNABAG [1] 4 > dchisq(MNABAG, df=1) [1] 0.02699548 > MNABAH <- ((bABAH - cABAH)^2 / (bABAH + cABAH)) > MNABAH [1] 1.8 > dchisq(MNABAH, df=1) [1] 0.1208951 > MNABBA <- ((bABBA - cABBA)^2 / (bABBA + cABBA)) > MNABBA [1] 7 > dchisq(MNABBA, df=1) [1] 0.004553343 > MNABBB <- ((bABBB - cABBB)^2 / (bABBB + cABBB)) > MNABBB [1] 1.8 > dchisq(MNABBB, df=1) [1] 0.1208951 > MNABBC <- ((bABBC - cABBC)^2 / (bABBC + cABBC)) > MNABBC [1] 9 > dchisq(MNABBC, df=1) [1] 0.001477283 > MNABBD <- ((bABBD - cABBD)^2 / (bABBD + cABBD)) > MNABBD [1] 10 > dchisq(MNABBD, df=1) [1] 0.0008500367 > MNABBE <- ((bABBE - cABBE)^2 / (bABBE + cABBE)) > MNABBE [1] 10 > dchisq(MNABBE, df=1) [1] 0.0008500367 > MNABBF <- ((bABBF - cABBF)^2 / (bABBF + cABBF)) > MNABBF [1] 5 > dchisq(MNABBF, df=1) [1] 0.01464498 > MNABBG <- ((bABBG - cABBG)^2 / (bABBG + cABBG)) > MNABBG [1] 7 > dchisq(MNABBG, df=1) [1] 0.004553343 > MNABBH <- ((bABBH - cABBH)^2 / (bABBH + cABBH)) > MNABBH [1] 8 > dchisq(MNABBH, df=1) [1] 0.002583373 > MNABCA <- ((bABCA - cABCA)^2 / (bABCA + cABCA)) > MNABCA [1] 8 > dchisq(MNABCA, df=1) [1] 0.002583373 > MNABCB <- ((bABCB - cABCB)^2 / (bABCB + cABCB)) > MNABCB [1] 2.666667 > dchisq(MNABCB, df=1) [1] 0.06439711 > MNABCC <- ((bABCC - cABCC)^2 / (bABCC + cABCC)) > MNABCC [1] 1.285714 > dchisq(MNABCC, df=1) [1] 0.1849901 > MNABCD <- ((bABCD - cABCD)^2 / (bABCD + cABCD)) > MNABCD [1] 10 > dchisq(MNABCD, df=1) [1] 0.0008500367 > MNABCE <- ((bABCE - cABCE)^2 / (bABCE + cABCE)) > MNABCE [1] 10 > dchisq(MNABCE, df=1) [1] 0.0008500367 > MNABCF <- ((bABCF - cABCF)^2 / (bABCF + cABCF)) > MNABCF [1] 8 > dchisq(MNABCF, df=1) [1] 0.002583373 > MNABCG <- ((bABCG - cABCG)^2 / (bABCG + cABCG)) > MNABCG [1] 9 > dchisq(MNABCG, df=1) [1] 0.001477283 > MNABCH <- ((bABCH - cABCH)^2 / (bABCH + cABCH)) > MNABCH [1] 8 > dchisq(MNABCH, df=1) [1] 0.002583373 > MNACAA <- ((bACAA - cACAA)^2 / (bACAA + cACAA)) > MNACAA [1] 9 > dchisq(MNACAA, df=1) [1] 0.001477283 > MNACAB <- ((bACAB - cACAB)^2 / (bACAB + cACAB)) > MNACAB [1] 2.666667 > dchisq(MNACAB, df=1) [1] 0.06439711 > MNACAC <- ((bACAC - cACAC)^2 / (bACAC + cACAC)) > MNACAC [1] NaN > dchisq(MNACAC, df=1) [1] NaN > MNACAD <- ((bACAD - cACAD)^2 / (bACAD + cACAD)) > MNACAD [1] 8 > dchisq(MNACAD, df=1) [1] 0.002583373 > MNACAE <- ((bACAE - cACAE)^2 / (bACAE + cACAE)) > MNACAE [1] 9 > dchisq(MNACAE, df=1) [1] 0.001477283 > MNACAF <- ((bACAF - cACAF)^2 / (bACAF + cACAF)) > MNACAF [1] 9 > dchisq(MNACAF, df=1) [1] 0.001477283 > MNACAG <- ((bACAG - cACAG)^2 / (bACAG + cACAG)) > MNACAG [1] 5 > dchisq(MNACAG, df=1) [1] 0.01464498 > MNACAH <- ((bACAH - cACAH)^2 / (bACAH + cACAH)) > MNACAH [1] 1 > dchisq(MNACAH, df=1) [1] 0.2419707 > MNACBA <- ((bACBA - cACBA)^2 / (bACBA + cACBA)) > MNACBA [1] 8 > dchisq(MNACBA, df=1) [1] 0.002583373 > MNACBB <- ((bACBB - cACBB)^2 / (bACBB + cACBB)) > MNACBB [1] 0 > dchisq(MNACBB, df=1) [1] Inf > MNACBC <- ((bACBC - cACBC)^2 / (bACBC + cACBC)) > MNACBC [1] 4 > dchisq(MNACBC, df=1) [1] 0.02699548 > MNACBD <- ((bACBD - cACBD)^2 / (bACBD + cACBD)) > MNACBD [1] 10 > dchisq(MNACBD, df=1) [1] 0.0008500367 > MNACBE <- ((bACBE - cACBE)^2 / (bACBE + cACBE)) > MNACBE [1] 10 > dchisq(MNACBE, df=1) [1] 0.0008500367 > MNACBF <- ((bACBF - cACBF)^2 / (bACBF + cACBF)) > MNACBF [1] 0 > dchisq(MNACBF, df=1) [1] Inf > MNACBG <- ((bACBG - cACBG)^2 / (bACBG + cACBG)) > MNACBG [1] 1 > dchisq(MNACBG, df=1) [1] 0.2419707 > MNACBH <- ((bACBH - cACBH)^2 / (bACBH + cACBH)) > MNACBH [1] 0 > dchisq(MNACBH, df=1) [1] Inf > MNACCA <- ((bACCA - cACCA)^2 / (bACCA + cACCA)) > MNACCA [1] 9 > dchisq(MNACCA, df=1) [1] 0.001477283 > MNACCB <- ((bACCB - cACCB)^2 / (bACCB + cACCB)) > MNACCB [1] 6 > dchisq(MNACCB, df=1) [1] 0.008108696 > MNACCC <- ((bACCC - cACCC)^2 / (bACCC + cACCC)) > MNACCC [1] 1 > dchisq(MNACCC, df=1) [1] 0.2419707 > MNACCD <- ((bACCD - cACCD)^2 / (bACCD + cACCD)) > MNACCD [1] 9 > dchisq(MNACCD, df=1) [1] 0.001477283 > MNACCE <- ((bACCE - cACCE)^2 / (bACCE + cACCE)) > MNACCE [1] 9 > dchisq(MNACCE, df=1) [1] 0.001477283 > MNACCF <- ((bACCF - cACCF)^2 / (bACCF + cACCF)) > MNACCF [1] 9 > dchisq(MNACCF, df=1) [1] 0.001477283 > MNACCG <- ((bACCG - cACCG)^2 / (bACCG + cACCG)) > MNACCG [1] 10 > dchisq(MNACCG, df=1) [1] 0.0008500367 > MNACCH <- ((bACCH - cACCH)^2 / (bACCH + cACCH)) > MNACCH [1] 9 > dchisq(MNACCH, df=1) [1] 0.001477283 > MNADAA <- ((bADAA - cADAA)^2 / (bADAA + cADAA)) > MNADAA [1] 10 > dchisq(MNADAA, df=1) [1] 0.0008500367 > MNADAB <- ((bADAB - cADAB)^2 / (bADAB + cADAB)) > MNADAB [1] 10 > dchisq(MNADAB, df=1) [1] 0.0008500367 > MNADAC <- ((bADAC - cADAC)^2 / (bADAC + cADAC)) > MNADAC [1] 8 > dchisq(MNADAC, df=1) [1] 0.002583373 > MNADAD <- ((bADAD - cADAD)^2 / (bADAD + cADAD)) > MNADAD [1] NaN > dchisq(MNADAD, df=1) [1] NaN > MNADAE <- ((bADAE - cADAE)^2 / (bADAE + cADAE)) > MNADAE [1] 1 > dchisq(MNADAE, df=1) [1] 0.2419707 > MNADAF <- ((bADAF - cADAF)^2 / (bADAF + cADAF)) > MNADAF [1] 10 > dchisq(MNADAF, df=1) [1] 0.0008500367 > MNADAG <- ((bADAG - cADAG)^2 / (bADAG + cADAG)) > MNADAG [1] 9 > dchisq(MNADAG, df=1) [1] 0.001477283 > MNADAH <- ((bADAH - cADAH)^2 / (bADAH + cADAH)) > MNADAH [1] 10 > dchisq(MNADAH, df=1) [1] 0.0008500367 > MNADBA <- ((bADBA - cADBA)^2 / (bADBA + cADBA)) > MNADBA [1] 10 > dchisq(MNADBA, df=1) [1] 0.0008500367 > MNADBB <- ((bADBB - cADBB)^2 / (bADBB + cADBB)) > MNADBB [1] 10 > dchisq(MNADBB, df=1) [1] 0.0008500367 > MNADBC <- ((bADBC - cADBC)^2 / (bADBC + cADBC)) > MNADBC [1] 4 > dchisq(MNADBC, df=1) [1] 0.02699548 > MNADBD <- ((bADBD - cADBD)^2 / (bADBD + cADBD)) > MNADBD [1] 4 > dchisq(MNADBD, df=1) [1] 0.02699548 > MNADBE <- ((bADBE - cADBE)^2 / (bADBE + cADBE)) > MNADBE [1] 5 > dchisq(MNADBE, df=1) [1] 0.01464498 > MNADBF <- ((bADBF - cADBF)^2 / (bADBF + cADBF)) > MNADBF [1] 9 > dchisq(MNADBF, df=1) [1] 0.001477283 > MNADBG <- ((bADBG - cADBG)^2 / (bADBG + cADBG)) > MNADBG [1] 6 > dchisq(MNADBG, df=1) [1] 0.008108696 > MNADBH <- ((bADBH - cADBH)^2 / (bADBH + cADBH)) > MNADBH [1] 8 > dchisq(MNADBH, df=1) [1] 0.002583373 > MNADCA <- ((bADCA - cADCA)^2 / (bADCA + cADCA)) > MNADCA [1] 10 > dchisq(MNADCA, df=1) [1] 0.0008500367 > MNADCB <- ((bADCB - cADCB)^2 / (bADCB + cADCB)) > MNADCB [1] 10 > dchisq(MNADCB, df=1) [1] 0.0008500367 > MNADCC <- ((bADCC - cADCC)^2 / (bADCC + cADCC)) > MNADCC [1] 10 > dchisq(MNADCC, df=1) [1] 0.0008500367 > MNADCD <- ((bADCD - cADCD)^2 / (bADCD + cADCD)) > MNADCD [1] 4 > dchisq(MNADCD, df=1) [1] 0.02699548 > MNADCE <- ((bADCE - cADCE)^2 / (bADCE + cADCE)) > MNADCE [1] 2 > dchisq(MNADCE, df=1) [1] 0.1037769 > MNADCF <- ((bADCF - cADCF)^2 / (bADCF + cADCF)) > MNADCF [1] 10 > dchisq(MNADCF, df=1) [1] 0.0008500367 > MNADCG <- ((bADCG - cADCG)^2 / (bADCG + cADCG)) > MNADCG [1] 10 > dchisq(MNADCG, df=1) [1] 0.0008500367 > MNADCH <- ((bADCH - cADCH)^2 / (bADCH + cADCH)) > MNADCH [1] 10 > dchisq(MNADCH, df=1) [1] 0.0008500367 > MNAEAA <- ((bAEAA - cAEAA)^2 / (bAEAA + cAEAA)) > MNAEAA [1] 10 > dchisq(MNAEAA, df=1) [1] 0.0008500367 > MNAEAB <- ((bAEAB - cAEAB)^2 / (bAEAB + cAEAB)) > MNAEAB [1] 10 > dchisq(MNAEAB, df=1) [1] 0.0008500367 > MNAEAC <- ((bAEAC - cAEAC)^2 / (bAEAC + cAEAC)) > MNAEAC [1] 9 > dchisq(MNAEAC, df=1) [1] 0.001477283 > MNAEAD <- ((bAEAD - cAEAD)^2 / (bAEAD + cAEAD)) > MNAEAD [1] 1 > dchisq(MNAEAD, df=1) [1] 0.2419707 > MNAEAE <- ((bAEAE - cAEAE)^2 / (bAEAE + cAEAE)) > MNAEAE [1] NaN > dchisq(MNAEAE, df=1) [1] NaN > MNAEAF <- ((bAEAF - cAEAF)^2 / (bAEAF + cAEAF)) > MNAEAF [1] 10 > dchisq(MNAEAF, df=1) [1] 0.0008500367 > MNAEAG <- ((bAEAG - cAEAG)^2 / (bAEAG + cAEAG)) > MNAEAG [1] 9 > dchisq(MNAEAG, df=1) [1] 0.001477283 > MNAEAH <- ((bAEAH - cAEAH)^2 / (bAEAH + cAEAH)) > MNAEAH [1] 10 > dchisq(MNAEAH, df=1) [1] 0.0008500367 > MNAEBA <- ((bAEBA - cAEBA)^2 / (bAEBA + cAEBA)) > MNAEBA [1] 10 > dchisq(MNAEBA, df=1) [1] 0.0008500367 > MNAEBB <- ((bAEBB - cAEBB)^2 / (bAEBB + cAEBB)) > MNAEBB [1] 10 > dchisq(MNAEBB, df=1) [1] 0.0008500367 > MNAEBC <- ((bAEBC - cAEBC)^2 / (bAEBC + cAEBC)) > MNAEBC [1] 3 > dchisq(MNAEBC, df=1) [1] 0.05139344 > MNAEBD <- ((bAEBD - cAEBD)^2 / (bAEBD + cAEBD)) > MNAEBD [1] 6 > dchisq(MNAEBD, df=1) [1] 0.008108696 > MNAEBE <- ((bAEBE - cAEBE)^2 / (bAEBE + cAEBE)) > MNAEBE [1] 4 > dchisq(MNAEBE, df=1) [1] 0.02699548 > MNAEBF <- ((bAEBF - cAEBF)^2 / (bAEBF + cAEBF)) > MNAEBF [1] 10 > dchisq(MNAEBF, df=1) [1] 0.0008500367 > MNAEBG <- ((bAEBG - cAEBG)^2 / (bAEBG + cAEBG)) > MNAEBG [1] 7 > dchisq(MNAEBG, df=1) [1] 0.004553343 > MNAEBH <- ((bAEBH - cAEBH)^2 / (bAEBH + cAEBH)) > MNAEBH [1] 8 > dchisq(MNAEBH, df=1) [1] 0.002583373 > MNAECA <- ((bAECA - cAECA)^2 / (bAECA + cAECA)) > MNAECA [1] 10 > dchisq(MNAECA, df=1) [1] 0.0008500367 > MNAECB <- ((bAECB - cAECB)^2 / (bAECB + cAECB)) > MNAECB [1] 10 > dchisq(MNAECB, df=1) [1] 0.0008500367 > MNAECC <- ((bAECC - cAECC)^2 / (bAECC + cAECC)) > MNAECC [1] 10 > dchisq(MNAECC, df=1) [1] 0.0008500367 > MNAECD <- ((bAECD - cAECD)^2 / (bAECD + cAECD)) > MNAECD [1] 3 > dchisq(MNAECD, df=1) [1] 0.05139344 > MNAECE <- ((bAECE - cAECE)^2 / (bAECE + cAECE)) > MNAECE [1] NaN > dchisq(MNAECE, df=1) [1] NaN > MNAECF <- ((bAECF - cAECF)^2 / (bAECF + cAECF)) > MNAECF [1] 10 > dchisq(MNAECF, df=1) [1] 0.0008500367 > MNAECG <- ((bAECG - cAECG)^2 / (bAECG + cAECG)) > MNAECG [1] 10 > dchisq(MNAECG, df=1) [1] 0.0008500367 > MNAECH <- ((bAECH - cAECH)^2 / (bAECH + cAECH)) > MNAECH [1] 10 > dchisq(MNAECH, df=1) [1] 0.0008500367 > MNAFAA <- ((bAFAA - cAFAA)^2 / (bAFAA + cAFAA)) > MNAFAA [1] 2 > dchisq(MNAFAA, df=1) [1] 0.1037769 > MNAFAB <- ((bAFAB - cAFAB)^2 / (bAFAB + cAFAB)) > MNAFAB [1] 8 > dchisq(MNAFAB, df=1) [1] 0.002583373 > MNAFAC <- ((bAFAC - cAFAC)^2 / (bAFAC + cAFAC)) > MNAFAC [1] 9 > dchisq(MNAFAC, df=1) [1] 0.001477283 > MNAFAD <- ((bAFAD - cAFAD)^2 / (bAFAD + cAFAD)) > MNAFAD [1] 10 > dchisq(MNAFAD, df=1) [1] 0.0008500367 > MNAFAE <- ((bAFAE - cAFAE)^2 / (bAFAE + cAFAE)) > MNAFAE [1] 10 > dchisq(MNAFAE, df=1) [1] 0.0008500367 > MNAFAF <- ((bAFAF - cAFAF)^2 / (bAFAF + cAFAF)) > MNAFAF [1] NaN > dchisq(MNAFAF, df=1) [1] NaN > MNAFAG <- ((bAFAG - cAFAG)^2 / (bAFAG + cAFAG)) > MNAFAG [1] 6 > dchisq(MNAFAG, df=1) [1] 0.008108696 > MNAFAH <- ((bAFAH - cAFAH)^2 / (bAFAH + cAFAH)) > MNAFAH [1] 7 > dchisq(MNAFAH, df=1) [1] 0.004553343 > MNAFBA <- ((bAFBA - cAFBA)^2 / (bAFBA + cAFBA)) > MNAFBA [1] 6 > dchisq(MNAFBA, df=1) [1] 0.008108696 > MNAFBB <- ((bAFBB - cAFBB)^2 / (bAFBB + cAFBB)) > MNAFBB [1] 8 > dchisq(MNAFBB, df=1) [1] 0.002583373 > MNAFBC <- ((bAFBC - cAFBC)^2 / (bAFBC + cAFBC)) > MNAFBC [1] 10 > dchisq(MNAFBC, df=1) [1] 0.0008500367 > MNAFBD <- ((bAFBD - cAFBD)^2 / (bAFBD + cAFBD)) > MNAFBD [1] 10 > dchisq(MNAFBD, df=1) [1] 0.0008500367 > MNAFBE <- ((bAFBE - cAFBE)^2 / (bAFBE + cAFBE)) > MNAFBE [1] 10 > dchisq(MNAFBE, df=1) [1] 0.0008500367 > MNAFBF <- ((bAFBF - cAFBF)^2 / (bAFBF + cAFBF)) > MNAFBF [1] 9 > dchisq(MNAFBF, df=1) [1] 0.001477283 > MNAFBG <- ((bAFBG - cAFBG)^2 / (bAFBG + cAFBG)) > MNAFBG [1] 10 > dchisq(MNAFBG, df=1) [1] 0.0008500367 > MNAFBH <- ((bAFBH - cAFBH)^2 / (bAFBH + cAFBH)) > MNAFBH [1] 10 > dchisq(MNAFBH, df=1) [1] 0.0008500367 > MNAFCA <- ((bAFCA - cAFCA)^2 / (bAFCA + cAFCA)) > MNAFCA [1] 2 > dchisq(MNAFCA, df=1) [1] 0.1037769 > MNAFCB <- ((bAFCB - cAFCB)^2 / (bAFCB + cAFCB)) > MNAFCB [1] 7 > dchisq(MNAFCB, df=1) [1] 0.004553343 > MNAFCC <- ((bAFCC - cAFCC)^2 / (bAFCC + cAFCC)) > MNAFCC [1] 8 > dchisq(MNAFCC, df=1) [1] 0.002583373 > MNAFCD <- ((bAFCD - cAFCD)^2 / (bAFCD + cAFCD)) > MNAFCD [1] 10 > dchisq(MNAFCD, df=1) [1] 0.0008500367 > MNAFCE <- ((bAFCE - cAFCE)^2 / (bAFCE + cAFCE)) > MNAFCE [1] 10 > dchisq(MNAFCE, df=1) [1] 0.0008500367 > MNAFCF <- ((bAFCF - cAFCF)^2 / (bAFCF + cAFCF)) > MNAFCF [1] 2 > dchisq(MNAFCF, df=1) [1] 0.1037769 > MNAFCG <- ((bAFCG - cAFCG)^2 / (bAFCG + cAFCG)) > MNAFCG [1] 5 > dchisq(MNAFCG, df=1) [1] 0.01464498 > MNAFCH <- ((bAFCH - cAFCH)^2 / (bAFCH + cAFCH)) > MNAFCH [1] 1 > dchisq(MNAFCH, df=1) [1] 0.2419707 > MNAGAA <- ((bAGAA - cAGAA)^2 / (bAGAA + cAGAA)) > MNAGAA [1] 5 > dchisq(MNAGAA, df=1) [1] 0.01464498 > MNAGAB <- ((bAGAB - cAGAB)^2 / (bAGAB + cAGAB)) > MNAGAB [1] 4 > dchisq(MNAGAB, df=1) [1] 0.02699548 > MNAGAC <- ((bAGAC - cAGAC)^2 / (bAGAC + cAGAC)) > MNAGAC [1] 5 > dchisq(MNAGAC, df=1) [1] 0.01464498 > MNAGAD <- ((bAGAD - cAGAD)^2 / (bAGAD + cAGAD)) > MNAGAD [1] 9 > dchisq(MNAGAD, df=1) [1] 0.001477283 > MNAGAE <- ((bAGAE - cAGAE)^2 / (bAGAE + cAGAE)) > MNAGAE [1] 9 > dchisq(MNAGAE, df=1) [1] 0.001477283 > MNAGAF <- ((bAGAF - cAGAF)^2 / (bAGAF + cAGAF)) > MNAGAF [1] 6 > dchisq(MNAGAF, df=1) [1] 0.008108696 > MNAGAG <- ((bAGAG - cAGAG)^2 / (bAGAG + cAGAG)) > MNAGAG [1] NaN > dchisq(MNAGAG, df=1) [1] NaN > MNAGAH <- ((bAGAH - cAGAH)^2 / (bAGAH + cAGAH)) > MNAGAH [1] 3.571429 > dchisq(MNAGAH, df=1) [1] 0.03539674 > MNAGBA <- ((bAGBA - cAGBA)^2 / (bAGBA + cAGBA)) > MNAGBA [1] 4 > dchisq(MNAGBA, df=1) [1] 0.02699548 > MNAGBB <- ((bAGBB - cAGBB)^2 / (bAGBB + cAGBB)) > MNAGBB [1] 4 > dchisq(MNAGBB, df=1) [1] 0.02699548 > MNAGBC <- ((bAGBC - cAGBC)^2 / (bAGBC + cAGBC)) > MNAGBC [1] 8 > dchisq(MNAGBC, df=1) [1] 0.002583373 > MNAGBD <- ((bAGBD - cAGBD)^2 / (bAGBD + cAGBD)) > MNAGBD [1] 10 > dchisq(MNAGBD, df=1) [1] 0.0008500367 > MNAGBE <- ((bAGBE - cAGBE)^2 / (bAGBE + cAGBE)) > MNAGBE [1] 9 > dchisq(MNAGBE, df=1) [1] 0.001477283 > MNAGBF <- ((bAGBF - cAGBF)^2 / (bAGBF + cAGBF)) > MNAGBF [1] 5 > dchisq(MNAGBF, df=1) [1] 0.01464498 > MNAGBG <- ((bAGBG - cAGBG)^2 / (bAGBG + cAGBG)) > MNAGBG [1] 6 > dchisq(MNAGBG, df=1) [1] 0.008108696 > MNAGBH <- ((bAGBH - cAGBH)^2 / (bAGBH + cAGBH)) > MNAGBH [1] 7 > dchisq(MNAGBH, df=1) [1] 0.004553343 > MNAGCA <- ((bAGCA - cAGCA)^2 / (bAGCA + cAGCA)) > MNAGCA [1] 6 > dchisq(MNAGCA, df=1) [1] 0.008108696 > MNAGCB <- ((bAGCB - cAGCB)^2 / (bAGCB + cAGCB)) > MNAGCB [1] 1 > dchisq(MNAGCB, df=1) [1] 0.2419707 > MNAGCC <- ((bAGCC - cAGCC)^2 / (bAGCC + cAGCC)) > MNAGCC [1] 5 > dchisq(MNAGCC, df=1) [1] 0.01464498 > MNAGCD <- ((bAGCD - cAGCD)^2 / (bAGCD + cAGCD)) > MNAGCD [1] 9 > dchisq(MNAGCD, df=1) [1] 0.001477283 > MNAGCE <- ((bAGCE - cAGCE)^2 / (bAGCE + cAGCE)) > MNAGCE [1] 9 > dchisq(MNAGCE, df=1) [1] 0.001477283 > MNAGCF <- ((bAGCF - cAGCF)^2 / (bAGCF + cAGCF)) > MNAGCF [1] 8 > dchisq(MNAGCF, df=1) [1] 0.002583373 > MNAGCG <- ((bAGCG - cAGCG)^2 / (bAGCG + cAGCG)) > MNAGCG [1] 9 > dchisq(MNAGCG, df=1) [1] 0.001477283 > MNAGCH <- ((bAGCH - cAGCH)^2 / (bAGCH + cAGCH)) > MNAGCH [1] 6 > dchisq(MNAGCH, df=1) [1] 0.008108696 > MNAHAA <- ((bAHAA - cAHAA)^2 / (bAHAA + cAHAA)) > MNAHAA [1] 8 > dchisq(MNAHAA, df=1) [1] 0.002583373 > MNAHAB <- ((bAHAB - cAHAB)^2 / (bAHAB + cAHAB)) > MNAHAB [1] 1.8 > dchisq(MNAHAB, df=1) [1] 0.1208951 > MNAHAC <- ((bAHAC - cAHAC)^2 / (bAHAC + cAHAC)) > MNAHAC [1] 1 > dchisq(MNAHAC, df=1) [1] 0.2419707 > MNAHAD <- ((bAHAD - cAHAD)^2 / (bAHAD + cAHAD)) > MNAHAD [1] 10 > dchisq(MNAHAD, df=1) [1] 0.0008500367 > MNAHAE <- ((bAHAE - cAHAE)^2 / (bAHAE + cAHAE)) > MNAHAE [1] 10 > dchisq(MNAHAE, df=1) [1] 0.0008500367 > MNAHAF <- ((bAHAF - cAHAF)^2 / (bAHAF + cAHAF)) > MNAHAF [1] 7 > dchisq(MNAHAF, df=1) [1] 0.004553343 > MNAHAG <- ((bAHAG - cAHAG)^2 / (bAHAG + cAHAG)) > MNAHAG [1] 3.571429 > dchisq(MNAHAG, df=1) [1] 0.03539674 > MNAHAH <- ((bAHAH - cAHAH)^2 / (bAHAH + cAHAH)) > MNAHAH [1] NaN > dchisq(MNAHAH, df=1) [1] NaN > MNAHBA <- ((bAHBA - cAHBA)^2 / (bAHBA + cAHBA)) > MNAHBA [1] 7 > dchisq(MNAHBA, df=1) [1] 0.004553343 > MNAHBB <- ((bAHBB - cAHBB)^2 / (bAHBB + cAHBB)) > MNAHBB [1] 1 > dchisq(MNAHBB, df=1) [1] 0.2419707 > MNAHBC <- ((bAHBC - cAHBC)^2 / (bAHBC + cAHBC)) > MNAHBC [1] 7 > dchisq(MNAHBC, df=1) [1] 0.004553343 > MNAHBD <- ((bAHBD - cAHBD)^2 / (bAHBD + cAHBD)) > MNAHBD [1] 10 > dchisq(MNAHBD, df=1) [1] 0.0008500367 > MNAHBE <- ((bAHBE - cAHBE)^2 / (bAHBE + cAHBE)) > MNAHBE [1] 10 > dchisq(MNAHBE, df=1) [1] 0.0008500367 > MNAHBF <- ((bAHBF - cAHBF)^2 / (bAHBF + cAHBF)) > MNAHBF [1] 4 > dchisq(MNAHBF, df=1) [1] 0.02699548 > MNAHBG <- ((bAHBG - cAHBG)^2 / (bAHBG + cAHBG)) > MNAHBG [1] 4 > dchisq(MNAHBG, df=1) [1] 0.02699548 > MNAHBH <- ((bAHBH - cAHBH)^2 / (bAHBH + cAHBH)) > MNAHBH [1] 4 > dchisq(MNAHBH, df=1) [1] 0.02699548 > MNAHCA <- ((bAHCA - cAHCA)^2 / (bAHCA + cAHCA)) > MNAHCA [1] 8 > dchisq(MNAHCA, df=1) [1] 0.002583373 > MNAHCB <- ((bAHCB - cAHCB)^2 / (bAHCB + cAHCB)) > MNAHCB [1] 2 > dchisq(MNAHCB, df=1) [1] 0.1037769 > MNAHCC <- ((bAHCC - cAHCC)^2 / (bAHCC + cAHCC)) > MNAHCC [1] 0.3333333 > dchisq(MNAHCC, df=1) [1] 0.584909 > MNAHCD <- ((bAHCD - cAHCD)^2 / (bAHCD + cAHCD)) > MNAHCD [1] 10 > dchisq(MNAHCD, df=1) [1] 0.0008500367 > MNAHCE <- ((bAHCE - cAHCE)^2 / (bAHCE + cAHCE)) > MNAHCE [1] 10 > dchisq(MNAHCE, df=1) [1] 0.0008500367 > MNAHCF <- ((bAHCF - cAHCF)^2 / (bAHCF + cAHCF)) > MNAHCF [1] 8 > dchisq(MNAHCF, df=1) [1] 0.002583373 > MNAHCG <- ((bAHCG - cAHCG)^2 / (bAHCG + cAHCG)) > MNAHCG [1] 10 > dchisq(MNAHCG, df=1) [1] 0.0008500367 > MNAHCH <- ((bAHCH - cAHCH)^2 / (bAHCH + cAHCH)) > MNAHCH [1] 8 > dchisq(MNAHCH, df=1) [1] 0.002583373 > MNBAAA <- ((bBAAA - cBAAA)^2 / (bBAAA + cBAAA)) > MNBAAA [1] 3 > dchisq(MNBAAA, df=1) [1] 0.05139344 > MNBAAB <- ((bBAAB - cBAAB)^2 / (bBAAB + cBAAB)) > MNBAAB [1] 7 > dchisq(MNBAAB, df=1) [1] 0.004553343 > MNBAAC <- ((bBAAC - cBAAC)^2 / (bBAAC + cBAAC)) > MNBAAC [1] 8 > dchisq(MNBAAC, df=1) [1] 0.002583373 > MNBAAD <- ((bBAAD - cBAAD)^2 / (bBAAD + cBAAD)) > MNBAAD [1] 10 > dchisq(MNBAAD, df=1) [1] 0.0008500367 > MNBAAE <- ((bBAAE - cBAAE)^2 / (bBAAE + cBAAE)) > MNBAAE [1] 10 > dchisq(MNBAAE, df=1) [1] 0.0008500367 > MNBAAF <- ((bBAAF - cBAAF)^2 / (bBAAF + cBAAF)) > MNBAAF [1] 6 > dchisq(MNBAAF, df=1) [1] 0.008108696 > MNBAAG <- ((bBAAG - cBAAG)^2 / (bBAAG + cBAAG)) > MNBAAG [1] 4 > dchisq(MNBAAG, df=1) [1] 0.02699548 > MNBAAH <- ((bBAAH - cBAAH)^2 / (bBAAH + cBAAH)) > MNBAAH [1] 7 > dchisq(MNBAAH, df=1) [1] 0.004553343 > MNBABA <- ((bBABA - cBABA)^2 / (bBABA + cBABA)) > MNBABA [1] NaN > dchisq(MNBABA, df=1) [1] NaN > MNBABB <- ((bBABB - cBABB)^2 / (bBABB + cBABB)) > MNBABB [1] 7 > dchisq(MNBABB, df=1) [1] 0.004553343 > MNBABC <- ((bBABC - cBABC)^2 / (bBABC + cBABC)) > MNBABC [1] 10 > dchisq(MNBABC, df=1) [1] 0.0008500367 > MNBABD <- ((bBABD - cBABD)^2 / (bBABD + cBABD)) > MNBABD [1] 10 > dchisq(MNBABD, df=1) [1] 0.0008500367 > MNBABE <- ((bBABE - cBABE)^2 / (bBABE + cBABE)) > MNBABE [1] 10 > dchisq(MNBABE, df=1) [1] 0.0008500367 > MNBABF <- ((bBABF - cBABF)^2 / (bBABF + cBABF)) > MNBABF [1] 8 > dchisq(MNBABF, df=1) [1] 0.002583373 > MNBABG <- ((bBABG - cBABG)^2 / (bBABG + cBABG)) > MNBABG [1] 9 > dchisq(MNBABG, df=1) [1] 0.001477283 > MNBABH <- ((bBABH - cBABH)^2 / (bBABH + cBABH)) > MNBABH [1] 8 > dchisq(MNBABH, df=1) [1] 0.002583373 > MNBACA <- ((bBACA - cBACA)^2 / (bBACA + cBACA)) > MNBACA [1] 4 > dchisq(MNBACA, df=1) [1] 0.02699548 > MNBACB <- ((bBACB - cBACB)^2 / (bBACB + cBACB)) > MNBACB [1] 7 > dchisq(MNBACB, df=1) [1] 0.004553343 > MNBACC <- ((bBACC - cBACC)^2 / (bBACC + cBACC)) > MNBACC [1] 7 > dchisq(MNBACC, df=1) [1] 0.004553343 > MNBACD <- ((bBACD - cBACD)^2 / (bBACD + cBACD)) > MNBACD [1] 10 > dchisq(MNBACD, df=1) [1] 0.0008500367 > MNBACE <- ((bBACE - cBACE)^2 / (bBACE + cBACE)) > MNBACE [1] 10 > dchisq(MNBACE, df=1) [1] 0.0008500367 > MNBACF <- ((bBACF - cBACF)^2 / (bBACF + cBACF)) > MNBACF [1] 7 > dchisq(MNBACF, df=1) [1] 0.004553343 > MNBACG <- ((bBACG - cBACG)^2 / (bBACG + cBACG)) > MNBACG [1] 8 > dchisq(MNBACG, df=1) [1] 0.002583373 > MNBACH <- ((bBACH - cBACH)^2 / (bBACH + cBACH)) > MNBACH [1] 4 > dchisq(MNBACH, df=1) [1] 0.02699548 > MNBBAA <- ((bBBAA - cBBAA)^2 / (bBBAA + cBBAA)) > MNBBAA [1] 9 > dchisq(MNBBAA, df=1) [1] 0.001477283 > MNBBAB <- ((bBBAB - cBBAB)^2 / (bBBAB + cBBAB)) > MNBBAB [1] 1.8 > dchisq(MNBBAB, df=1) [1] 0.1208951 > MNBBAC <- ((bBBAC - cBBAC)^2 / (bBBAC + cBBAC)) > MNBBAC [1] 0 > dchisq(MNBBAC, df=1) [1] Inf > MNBBAD <- ((bBBAD - cBBAD)^2 / (bBBAD + cBBAD)) > MNBBAD [1] 10 > dchisq(MNBBAD, df=1) [1] 0.0008500367 > MNBBAE <- ((bBBAE - cBBAE)^2 / (bBBAE + cBBAE)) > MNBBAE [1] 10 > dchisq(MNBBAE, df=1) [1] 0.0008500367 > MNBBAF <- ((bBBAF - cBBAF)^2 / (bBBAF + cBBAF)) > MNBBAF [1] 8 > dchisq(MNBBAF, df=1) [1] 0.002583373 > MNBBAG <- ((bBBAG - cBBAG)^2 / (bBBAG + cBBAG)) > MNBBAG [1] 4 > dchisq(MNBBAG, df=1) [1] 0.02699548 > MNBBAH <- ((bBBAH - cBBAH)^2 / (bBBAH + cBBAH)) > MNBBAH [1] 1 > dchisq(MNBBAH, df=1) [1] 0.2419707 > MNBBBA <- ((bBBBA - cBBBA)^2 / (bBBBA + cBBBA)) > MNBBBA [1] 7 > dchisq(MNBBBA, df=1) [1] 0.004553343 > MNBBBB <- ((bBBBB - cBBBB)^2 / (bBBBB + cBBBB)) > MNBBBB [1] NaN > dchisq(MNBBBB, df=1) [1] NaN > MNBBBC <- ((bBBBC - cBBBC)^2 / (bBBBC + cBBBC)) > MNBBBC [1] 7 > dchisq(MNBBBC, df=1) [1] 0.004553343 > MNBBBD <- ((bBBBD - cBBBD)^2 / (bBBBD + cBBBD)) > MNBBBD [1] 10 > dchisq(MNBBBD, df=1) [1] 0.0008500367 > MNBBBE <- ((bBBBE - cBBBE)^2 / (bBBBE + cBBBE)) > MNBBBE [1] 10 > dchisq(MNBBBE, df=1) [1] 0.0008500367 > MNBBBF <- ((bBBBF - cBBBF)^2 / (bBBBF + cBBBF)) > MNBBBF [1] NaN > dchisq(MNBBBF, df=1) [1] NaN > MNBBBG <- ((bBBBG - cBBBG)^2 / (bBBBG + cBBBG)) > MNBBBG [1] 4 > dchisq(MNBBBG, df=1) [1] 0.02699548 > MNBBBH <- ((bBBBH - cBBBH)^2 / (bBBBH + cBBBH)) > MNBBBH [1] 3 > dchisq(MNBBBH, df=1) [1] 0.05139344 > MNBBCA <- ((bBBCA - cBBCA)^2 / (bBBCA + cBBCA)) > MNBBCA [1] 9 > dchisq(MNBBCA, df=1) [1] 0.001477283 > MNBBCB <- ((bBBCB - cBBCB)^2 / (bBBCB + cBBCB)) > MNBBCB [1] 4 > dchisq(MNBBCB, df=1) [1] 0.02699548 > MNBBCC <- ((bBBCC - cBBCC)^2 / (bBBCC + cBBCC)) > MNBBCC [1] 0 > dchisq(MNBBCC, df=1) [1] Inf > MNBBCD <- ((bBBCD - cBBCD)^2 / (bBBCD + cBBCD)) > MNBBCD [1] 10 > dchisq(MNBBCD, df=1) [1] 0.0008500367 > MNBBCE <- ((bBBCE - cBBCE)^2 / (bBBCE + cBBCE)) > MNBBCE [1] 10 > dchisq(MNBBCE, df=1) [1] 0.0008500367 > MNBBCF <- ((bBBCF - cBBCF)^2 / (bBBCF + cBBCF)) > MNBBCF [1] 9 > dchisq(MNBBCF, df=1) [1] 0.001477283 > MNBBCG <- ((bBBCG - cBBCG)^2 / (bBBCG + cBBCG)) > MNBBCG [1] 9 > dchisq(MNBBCG, df=1) [1] 0.001477283 > MNBBCH <- ((bBBCH - cBBCH)^2 / (bBBCH + cBBCH)) > MNBBCH [1] 9 > dchisq(MNBBCH, df=1) [1] 0.001477283 > MNBCAA <- ((bBCAA - cBCAA)^2 / (bBCAA + cBCAA)) > MNBCAA [1] 10 > dchisq(MNBCAA, df=1) [1] 0.0008500367 > MNBCAB <- ((bBCAB - cBCAB)^2 / (bBCAB + cBCAB)) > MNBCAB [1] 9 > dchisq(MNBCAB, df=1) [1] 0.001477283 > MNBCAC <- ((bBCAC - cBCAC)^2 / (bBCAC + cBCAC)) > MNBCAC [1] 4 > dchisq(MNBCAC, df=1) [1] 0.02699548 > MNBCAD <- ((bBCAD - cBCAD)^2 / (bBCAD + cBCAD)) > MNBCAD [1] 4 > dchisq(MNBCAD, df=1) [1] 0.02699548 > MNBCAE <- ((bBCAE - cBCAE)^2 / (bBCAE + cBCAE)) > MNBCAE [1] 3 > dchisq(MNBCAE, df=1) [1] 0.05139344 > MNBCAF <- ((bBCAF - cBCAF)^2 / (bBCAF + cBCAF)) > MNBCAF [1] 10 > dchisq(MNBCAF, df=1) [1] 0.0008500367 > MNBCAG <- ((bBCAG - cBCAG)^2 / (bBCAG + cBCAG)) > MNBCAG [1] 8 > dchisq(MNBCAG, df=1) [1] 0.002583373 > MNBCAH <- ((bBCAH - cBCAH)^2 / (bBCAH + cBCAH)) > MNBCAH [1] 7 > dchisq(MNBCAH, df=1) [1] 0.004553343 > MNBCBA <- ((bBCBA - cBCBA)^2 / (bBCBA + cBCBA)) > MNBCBA [1] 10 > dchisq(MNBCBA, df=1) [1] 0.0008500367 > MNBCBB <- ((bBCBB - cBCBB)^2 / (bBCBB + cBCBB)) > MNBCBB [1] 7 > dchisq(MNBCBB, df=1) [1] 0.004553343 > MNBCBC <- ((bBCBC - cBCBC)^2 / (bBCBC + cBCBC)) > MNBCBC [1] NaN > dchisq(MNBCBC, df=1) [1] NaN > MNBCBD <- ((bBCBD - cBCBD)^2 / (bBCBD + cBCBD)) > MNBCBD [1] 10 > dchisq(MNBCBD, df=1) [1] 0.0008500367 > MNBCBE <- ((bBCBE - cBCBE)^2 / (bBCBE + cBCBE)) > MNBCBE [1] 5 > dchisq(MNBCBE, df=1) [1] 0.01464498 > MNBCBF <- ((bBCBF - cBCBF)^2 / (bBCBF + cBCBF)) > MNBCBF [1] 5 > dchisq(MNBCBF, df=1) [1] 0.01464498 > MNBCBG <- ((bBCBG - cBCBG)^2 / (bBCBG + cBCBG)) > MNBCBG [1] 3 > dchisq(MNBCBG, df=1) [1] 0.05139344 > MNBCBH <- ((bBCBH - cBCBH)^2 / (bBCBH + cBCBH)) > MNBCBH [1] 4 > dchisq(MNBCBH, df=1) [1] 0.02699548 > MNBCCA <- ((bBCCA - cBCCA)^2 / (bBCCA + cBCCA)) > MNBCCA [1] 10 > dchisq(MNBCCA, df=1) [1] 0.0008500367 > MNBCCB <- ((bBCCB - cBCCB)^2 / (bBCCB + cBCCB)) > MNBCCB [1] 10 > dchisq(MNBCCB, df=1) [1] 0.0008500367 > MNBCCC <- ((bBCCC - cBCCC)^2 / (bBCCC + cBCCC)) > MNBCCC [1] 7 > dchisq(MNBCCC, df=1) [1] 0.004553343 > MNBCCD <- ((bBCCD - cBCCD)^2 / (bBCCD + cBCCD)) > MNBCCD [1] 5 > dchisq(MNBCCD, df=1) [1] 0.01464498 > MNBCCE <- ((bBCCE - cBCCE)^2 / (bBCCE + cBCCE)) > MNBCCE [1] 4 > dchisq(MNBCCE, df=1) [1] 0.02699548 > MNBCCF <- ((bBCCF - cBCCF)^2 / (bBCCF + cBCCF)) > MNBCCF [1] 10 > dchisq(MNBCCF, df=1) [1] 0.0008500367 > MNBCCG <- ((bBCCG - cBCCG)^2 / (bBCCG + cBCCG)) > MNBCCG [1] 10 > dchisq(MNBCCG, df=1) [1] 0.0008500367 > MNBCCH <- ((bBCCH - cBCCH)^2 / (bBCCH + cBCCH)) > MNBCCH [1] 10 > dchisq(MNBCCH, df=1) [1] 0.0008500367 > MNBDAA <- ((bBDAA - cBDAA)^2 / (bBDAA + cBDAA)) > MNBDAA [1] 10 > dchisq(MNBDAA, df=1) [1] 0.0008500367 > MNBDAB <- ((bBDAB - cBDAB)^2 / (bBDAB + cBDAB)) > MNBDAB [1] 10 > dchisq(MNBDAB, df=1) [1] 0.0008500367 > MNBDAC <- ((bBDAC - cBDAC)^2 / (bBDAC + cBDAC)) > MNBDAC [1] 10 > dchisq(MNBDAC, df=1) [1] 0.0008500367 > MNBDAD <- ((bBDAD - cBDAD)^2 / (bBDAD + cBDAD)) > MNBDAD [1] 4 > dchisq(MNBDAD, df=1) [1] 0.02699548 > MNBDAE <- ((bBDAE - cBDAE)^2 / (bBDAE + cBDAE)) > MNBDAE [1] 6 > dchisq(MNBDAE, df=1) [1] 0.008108696 > MNBDAF <- ((bBDAF - cBDAF)^2 / (bBDAF + cBDAF)) > MNBDAF [1] 10 > dchisq(MNBDAF, df=1) [1] 0.0008500367 > MNBDAG <- ((bBDAG - cBDAG)^2 / (bBDAG + cBDAG)) > MNBDAG [1] 10 > dchisq(MNBDAG, df=1) [1] 0.0008500367 > MNBDAH <- ((bBDAH - cBDAH)^2 / (bBDAH + cBDAH)) > MNBDAH [1] 10 > dchisq(MNBDAH, df=1) [1] 0.0008500367 > MNBDBA <- ((bBDBA - cBDBA)^2 / (bBDBA + cBDBA)) > MNBDBA [1] 10 > dchisq(MNBDBA, df=1) [1] 0.0008500367 > MNBDBB <- ((bBDBB - cBDBB)^2 / (bBDBB + cBDBB)) > MNBDBB [1] 10 > dchisq(MNBDBB, df=1) [1] 0.0008500367 > MNBDBC <- ((bBDBC - cBDBC)^2 / (bBDBC + cBDBC)) > MNBDBC [1] 10 > dchisq(MNBDBC, df=1) [1] 0.0008500367 > MNBDBD <- ((bBDBD - cBDBD)^2 / (bBDBD + cBDBD)) > MNBDBD [1] NaN > dchisq(MNBDBD, df=1) [1] NaN > MNBDBE <- ((bBDBE - cBDBE)^2 / (bBDBE + cBDBE)) > MNBDBE [1] 1 > dchisq(MNBDBE, df=1) [1] 0.2419707 > MNBDBF <- ((bBDBF - cBDBF)^2 / (bBDBF + cBDBF)) > MNBDBF [1] 10 > dchisq(MNBDBF, df=1) [1] 0.0008500367 > MNBDBG <- ((bBDBG - cBDBG)^2 / (bBDBG + cBDBG)) > MNBDBG [1] 10 > dchisq(MNBDBG, df=1) [1] 0.0008500367 > MNBDBH <- ((bBDBH - cBDBH)^2 / (bBDBH + cBDBH)) > MNBDBH [1] 10 > dchisq(MNBDBH, df=1) [1] 0.0008500367 > MNBDCA <- ((bBDCA - cBDCA)^2 / (bBDCA + cBDCA)) > MNBDCA [1] 10 > dchisq(MNBDCA, df=1) [1] 0.0008500367 > MNBDCB <- ((bBDCB - cBDCB)^2 / (bBDCB + cBDCB)) > MNBDCB [1] 10 > dchisq(MNBDCB, df=1) [1] 0.0008500367 > MNBDCC <- ((bBDCC - cBDCC)^2 / (bBDCC + cBDCC)) > MNBDCC [1] 10 > dchisq(MNBDCC, df=1) [1] 0.0008500367 > MNBDCD <- ((bBDCD - cBDCD)^2 / (bBDCD + cBDCD)) > MNBDCD [1] 2 > dchisq(MNBDCD, df=1) [1] 0.1037769 > MNBDCE <- ((bBDCE - cBDCE)^2 / (bBDCE + cBDCE)) > MNBDCE [1] 4 > dchisq(MNBDCE, df=1) [1] 0.02699548 > MNBDCF <- ((bBDCF - cBDCF)^2 / (bBDCF + cBDCF)) > MNBDCF [1] 10 > dchisq(MNBDCF, df=1) [1] 0.0008500367 > MNBDCG <- ((bBDCG - cBDCG)^2 / (bBDCG + cBDCG)) > MNBDCG [1] 10 > dchisq(MNBDCG, df=1) [1] 0.0008500367 > MNBDCH <- ((bBDCH - cBDCH)^2 / (bBDCH + cBDCH)) > MNBDCH [1] 10 > dchisq(MNBDCH, df=1) [1] 0.0008500367 > MNBEAA <- ((bBEAA - cBEAA)^2 / (bBEAA + cBEAA)) > MNBEAA [1] 10 > dchisq(MNBEAA, df=1) [1] 0.0008500367 > MNBEAB <- ((bBEAB - cBEAB)^2 / (bBEAB + cBEAB)) > MNBEAB [1] 10 > dchisq(MNBEAB, df=1) [1] 0.0008500367 > MNBEAC <- ((bBEAC - cBEAC)^2 / (bBEAC + cBEAC)) > MNBEAC [1] 10 > dchisq(MNBEAC, df=1) [1] 0.0008500367 > MNBEAD <- ((bBEAD - cBEAD)^2 / (bBEAD + cBEAD)) > MNBEAD [1] 5 > dchisq(MNBEAD, df=1) [1] 0.01464498 > MNBEAE <- ((bBEAE - cBEAE)^2 / (bBEAE + cBEAE)) > MNBEAE [1] 4 > dchisq(MNBEAE, df=1) [1] 0.02699548 > MNBEAF <- ((bBEAF - cBEAF)^2 / (bBEAF + cBEAF)) > MNBEAF [1] 10 > dchisq(MNBEAF, df=1) [1] 0.0008500367 > MNBEAG <- ((bBEAG - cBEAG)^2 / (bBEAG + cBEAG)) > MNBEAG [1] 9 > dchisq(MNBEAG, df=1) [1] 0.001477283 > MNBEAH <- ((bBEAH - cBEAH)^2 / (bBEAH + cBEAH)) > MNBEAH [1] 10 > dchisq(MNBEAH, df=1) [1] 0.0008500367 > MNBEBA <- ((bBEBA - cBEBA)^2 / (bBEBA + cBEBA)) > MNBEBA [1] 10 > dchisq(MNBEBA, df=1) [1] 0.0008500367 > MNBEBB <- ((bBEBB - cBEBB)^2 / (bBEBB + cBEBB)) > MNBEBB [1] 10 > dchisq(MNBEBB, df=1) [1] 0.0008500367 > MNBEBC <- ((bBEBC - cBEBC)^2 / (bBEBC + cBEBC)) > MNBEBC [1] 5 > dchisq(MNBEBC, df=1) [1] 0.01464498 > MNBEBD <- ((bBEBD - cBEBD)^2 / (bBEBD + cBEBD)) > MNBEBD [1] 1 > dchisq(MNBEBD, df=1) [1] 0.2419707 > MNBEBE <- ((bBEBE - cBEBE)^2 / (bBEBE + cBEBE)) > MNBEBE [1] NaN > dchisq(MNBEBE, df=1) [1] NaN > MNBEBF <- ((bBEBF - cBEBF)^2 / (bBEBF + cBEBF)) > MNBEBF [1] 10 > dchisq(MNBEBF, df=1) [1] 0.0008500367 > MNBEBG <- ((bBEBG - cBEBG)^2 / (bBEBG + cBEBG)) > MNBEBG [1] 9 > dchisq(MNBEBG, df=1) [1] 0.001477283 > MNBEBH <- ((bBEBH - cBEBH)^2 / (bBEBH + cBEBH)) > MNBEBH [1] 10 > dchisq(MNBEBH, df=1) [1] 0.0008500367 > MNBECA <- ((bBECA - cBECA)^2 / (bBECA + cBECA)) > MNBECA [1] 10 > dchisq(MNBECA, df=1) [1] 0.0008500367 > MNBECB <- ((bBECB - cBECB)^2 / (bBECB + cBECB)) > MNBECB [1] 10 > dchisq(MNBECB, df=1) [1] 0.0008500367 > MNBECC <- ((bBECC - cBECC)^2 / (bBECC + cBECC)) > MNBECC [1] 10 > dchisq(MNBECC, df=1) [1] 0.0008500367 > MNBECD <- ((bBECD - cBECD)^2 / (bBECD + cBECD)) > MNBECD [1] 1 > dchisq(MNBECD, df=1) [1] 0.2419707 > MNBECE <- ((bBECE - cBECE)^2 / (bBECE + cBECE)) > MNBECE [1] 5 > dchisq(MNBECE, df=1) [1] 0.01464498 > MNBECF <- ((bBECF - cBECF)^2 / (bBECF + cBECF)) > MNBECF [1] 10 > dchisq(MNBECF, df=1) [1] 0.0008500367 > MNBECG <- ((bBECG - cBECG)^2 / (bBECG + cBECG)) > MNBECG [1] 10 > dchisq(MNBECG, df=1) [1] 0.0008500367 > MNBECH <- ((bBECH - cBECH)^2 / (bBECH + cBECH)) > MNBECH [1] 10 > dchisq(MNBECH, df=1) [1] 0.0008500367 > MNBFAA <- ((bBFAA - cBFAA)^2 / (bBFAA + cBFAA)) > MNBFAA [1] 9 > dchisq(MNBFAA, df=1) [1] 0.001477283 > MNBFAB <- ((bBFAB - cBFAB)^2 / (bBFAB + cBFAB)) > MNBFAB [1] 5 > dchisq(MNBFAB, df=1) [1] 0.01464498 > MNBFAC <- ((bBFAC - cBFAC)^2 / (bBFAC + cBFAC)) > MNBFAC [1] 0 > dchisq(MNBFAC, df=1) [1] Inf > MNBFAD <- ((bBFAD - cBFAD)^2 / (bBFAD + cBFAD)) > MNBFAD [1] 9 > dchisq(MNBFAD, df=1) [1] 0.001477283 > MNBFAE <- ((bBFAE - cBFAE)^2 / (bBFAE + cBFAE)) > MNBFAE [1] 10 > dchisq(MNBFAE, df=1) [1] 0.0008500367 > MNBFAF <- ((bBFAF - cBFAF)^2 / (bBFAF + cBFAF)) > MNBFAF [1] 9 > dchisq(MNBFAF, df=1) [1] 0.001477283 > MNBFAG <- ((bBFAG - cBFAG)^2 / (bBFAG + cBFAG)) > MNBFAG [1] 5 > dchisq(MNBFAG, df=1) [1] 0.01464498 > MNBFAH <- ((bBFAH - cBFAH)^2 / (bBFAH + cBFAH)) > MNBFAH [1] 4 > dchisq(MNBFAH, df=1) [1] 0.02699548 > MNBFBA <- ((bBFBA - cBFBA)^2 / (bBFBA + cBFBA)) > MNBFBA [1] 8 > dchisq(MNBFBA, df=1) [1] 0.002583373 > MNBFBB <- ((bBFBB - cBFBB)^2 / (bBFBB + cBFBB)) > MNBFBB [1] NaN > dchisq(MNBFBB, df=1) [1] NaN > MNBFBC <- ((bBFBC - cBFBC)^2 / (bBFBC + cBFBC)) > MNBFBC [1] 5 > dchisq(MNBFBC, df=1) [1] 0.01464498 > MNBFBD <- ((bBFBD - cBFBD)^2 / (bBFBD + cBFBD)) > MNBFBD [1] 10 > dchisq(MNBFBD, df=1) [1] 0.0008500367 > MNBFBE <- ((bBFBE - cBFBE)^2 / (bBFBE + cBFBE)) > MNBFBE [1] 10 > dchisq(MNBFBE, df=1) [1] 0.0008500367 > MNBFBF <- ((bBFBF - cBFBF)^2 / (bBFBF + cBFBF)) > MNBFBF [1] NaN > dchisq(MNBFBF, df=1) [1] NaN > MNBFBG <- ((bBFBG - cBFBG)^2 / (bBFBG + cBFBG)) > MNBFBG [1] 3 > dchisq(MNBFBG, df=1) [1] 0.05139344 > MNBFBH <- ((bBFBH - cBFBH)^2 / (bBFBH + cBFBH)) > MNBFBH [1] NaN > dchisq(MNBFBH, df=1) [1] NaN > MNBFCA <- ((bBFCA - cBFCA)^2 / (bBFCA + cBFCA)) > MNBFCA [1] 9 > dchisq(MNBFCA, df=1) [1] 0.001477283 > MNBFCB <- ((bBFCB - cBFCB)^2 / (bBFCB + cBFCB)) > MNBFCB [1] 6 > dchisq(MNBFCB, df=1) [1] 0.008108696 > MNBFCC <- ((bBFCC - cBFCC)^2 / (bBFCC + cBFCC)) > MNBFCC [1] 1 > dchisq(MNBFCC, df=1) [1] 0.2419707 > MNBFCD <- ((bBFCD - cBFCD)^2 / (bBFCD + cBFCD)) > MNBFCD [1] 9 > dchisq(MNBFCD, df=1) [1] 0.001477283 > MNBFCE <- ((bBFCE - cBFCE)^2 / (bBFCE + cBFCE)) > MNBFCE [1] 10 > dchisq(MNBFCE, df=1) [1] 0.0008500367 > MNBFCF <- ((bBFCF - cBFCF)^2 / (bBFCF + cBFCF)) > MNBFCF [1] 9 > dchisq(MNBFCF, df=1) [1] 0.001477283 > MNBFCG <- ((bBFCG - cBFCG)^2 / (bBFCG + cBFCG)) > MNBFCG [1] 10 > dchisq(MNBFCG, df=1) [1] 0.0008500367 > MNBFCH <- ((bBFCH - cBFCH)^2 / (bBFCH + cBFCH)) > MNBFCH [1] 9 > dchisq(MNBFCH, df=1) [1] 0.001477283 > MNBGAA <- ((bBGAA - cBGAA)^2 / (bBGAA + cBGAA)) > MNBGAA [1] 10 > dchisq(MNBGAA, df=1) [1] 0.0008500367 > MNBGAB <- ((bBGAB - cBGAB)^2 / (bBGAB + cBGAB)) > MNBGAB [1] 7 > dchisq(MNBGAB, df=1) [1] 0.004553343 > MNBGAC <- ((bBGAC - cBGAC)^2 / (bBGAC + cBGAC)) > MNBGAC [1] 1 > dchisq(MNBGAC, df=1) [1] 0.2419707 > MNBGAD <- ((bBGAD - cBGAD)^2 / (bBGAD + cBGAD)) > MNBGAD [1] 6 > dchisq(MNBGAD, df=1) [1] 0.008108696 > MNBGAE <- ((bBGAE - cBGAE)^2 / (bBGAE + cBGAE)) > MNBGAE [1] 7 > dchisq(MNBGAE, df=1) [1] 0.004553343 > MNBGAF <- ((bBGAF - cBGAF)^2 / (bBGAF + cBGAF)) > MNBGAF [1] 10 > dchisq(MNBGAF, df=1) [1] 0.0008500367 > MNBGAG <- ((bBGAG - cBGAG)^2 / (bBGAG + cBGAG)) > MNBGAG [1] 6 > dchisq(MNBGAG, df=1) [1] 0.008108696 > MNBGAH <- ((bBGAH - cBGAH)^2 / (bBGAH + cBGAH)) > MNBGAH [1] 4 > dchisq(MNBGAH, df=1) [1] 0.02699548 > MNBGBA <- ((bBGBA - cBGBA)^2 / (bBGBA + cBGBA)) > MNBGBA [1] 9 > dchisq(MNBGBA, df=1) [1] 0.001477283 > MNBGBB <- ((bBGBB - cBGBB)^2 / (bBGBB + cBGBB)) > MNBGBB [1] 4 > dchisq(MNBGBB, df=1) [1] 0.02699548 > MNBGBC <- ((bBGBC - cBGBC)^2 / (bBGBC + cBGBC)) > MNBGBC [1] 3 > dchisq(MNBGBC, df=1) [1] 0.05139344 > MNBGBD <- ((bBGBD - cBGBD)^2 / (bBGBD + cBGBD)) > MNBGBD [1] 10 > dchisq(MNBGBD, df=1) [1] 0.0008500367 > MNBGBE <- ((bBGBE - cBGBE)^2 / (bBGBE + cBGBE)) > MNBGBE [1] 9 > dchisq(MNBGBE, df=1) [1] 0.001477283 > MNBGBF <- ((bBGBF - cBGBF)^2 / (bBGBF + cBGBF)) > MNBGBF [1] 3 > dchisq(MNBGBF, df=1) [1] 0.05139344 > MNBGBG <- ((bBGBG - cBGBG)^2 / (bBGBG + cBGBG)) > MNBGBG [1] NaN > dchisq(MNBGBG, df=1) [1] NaN > MNBGBH <- ((bBGBH - cBGBH)^2 / (bBGBH + cBGBH)) > MNBGBH [1] 1 > dchisq(MNBGBH, df=1) [1] 0.2419707 > MNBGCA <- ((bBGCA - cBGCA)^2 / (bBGCA + cBGCA)) > MNBGCA [1] 10 > dchisq(MNBGCA, df=1) [1] 0.0008500367 > MNBGCB <- ((bBGCB - cBGCB)^2 / (bBGCB + cBGCB)) > MNBGCB [1] 7 > dchisq(MNBGCB, df=1) [1] 0.004553343 > MNBGCC <- ((bBGCC - cBGCC)^2 / (bBGCC + cBGCC)) > MNBGCC [1] 3 > dchisq(MNBGCC, df=1) [1] 0.05139344 > MNBGCD <- ((bBGCD - cBGCD)^2 / (bBGCD + cBGCD)) > MNBGCD [1] 9 > dchisq(MNBGCD, df=1) [1] 0.001477283 > MNBGCE <- ((bBGCE - cBGCE)^2 / (bBGCE + cBGCE)) > MNBGCE [1] 8 > dchisq(MNBGCE, df=1) [1] 0.002583373 > MNBGCF <- ((bBGCF - cBGCF)^2 / (bBGCF + cBGCF)) > MNBGCF [1] 10 > dchisq(MNBGCF, df=1) [1] 0.0008500367 > MNBGCG <- ((bBGCG - cBGCG)^2 / (bBGCG + cBGCG)) > MNBGCG [1] 10 > dchisq(MNBGCG, df=1) [1] 0.0008500367 > MNBGCH <- ((bBGCH - cBGCH)^2 / (bBGCH + cBGCH)) > MNBGCH [1] 10 > dchisq(MNBGCH, df=1) [1] 0.0008500367 > MNBHAA <- ((bBHAA - cBHAA)^2 / (bBHAA + cBHAA)) > MNBHAA [1] 9 > dchisq(MNBHAA, df=1) [1] 0.001477283 > MNBHAB <- ((bBHAB - cBHAB)^2 / (bBHAB + cBHAB)) > MNBHAB [1] 8 > dchisq(MNBHAB, df=1) [1] 0.002583373 > MNBHAC <- ((bBHAC - cBHAC)^2 / (bBHAC + cBHAC)) > MNBHAC [1] 0 > dchisq(MNBHAC, df=1) [1] Inf > MNBHAD <- ((bBHAD - cBHAD)^2 / (bBHAD + cBHAD)) > MNBHAD [1] 8 > dchisq(MNBHAD, df=1) [1] 0.002583373 > MNBHAE <- ((bBHAE - cBHAE)^2 / (bBHAE + cBHAE)) > MNBHAE [1] 8 > dchisq(MNBHAE, df=1) [1] 0.002583373 > MNBHAF <- ((bBHAF - cBHAF)^2 / (bBHAF + cBHAF)) > MNBHAF [1] 10 > dchisq(MNBHAF, df=1) [1] 0.0008500367 > MNBHAG <- ((bBHAG - cBHAG)^2 / (bBHAG + cBHAG)) > MNBHAG [1] 7 > dchisq(MNBHAG, df=1) [1] 0.004553343 > MNBHAH <- ((bBHAH - cBHAH)^2 / (bBHAH + cBHAH)) > MNBHAH [1] 4 > dchisq(MNBHAH, df=1) [1] 0.02699548 > MNBHBA <- ((bBHBA - cBHBA)^2 / (bBHBA + cBHBA)) > MNBHBA [1] 8 > dchisq(MNBHBA, df=1) [1] 0.002583373 > MNBHBB <- ((bBHBB - cBHBB)^2 / (bBHBB + cBHBB)) > MNBHBB [1] 3 > dchisq(MNBHBB, df=1) [1] 0.05139344 > MNBHBC <- ((bBHBC - cBHBC)^2 / (bBHBC + cBHBC)) > MNBHBC [1] 4 > dchisq(MNBHBC, df=1) [1] 0.02699548 > MNBHBD <- ((bBHBD - cBHBD)^2 / (bBHBD + cBHBD)) > MNBHBD [1] 10 > dchisq(MNBHBD, df=1) [1] 0.0008500367 > MNBHBE <- ((bBHBE - cBHBE)^2 / (bBHBE + cBHBE)) > MNBHBE [1] 10 > dchisq(MNBHBE, df=1) [1] 0.0008500367 > MNBHBF <- ((bBHBF - cBHBF)^2 / (bBHBF + cBHBF)) > MNBHBF [1] NaN > dchisq(MNBHBF, df=1) [1] NaN > MNBHBG <- ((bBHBG - cBHBG)^2 / (bBHBG + cBHBG)) > MNBHBG [1] 1 > dchisq(MNBHBG, df=1) [1] 0.2419707 > MNBHBH <- ((bBHBH - cBHBH)^2 / (bBHBH + cBHBH)) > MNBHBH [1] NaN > dchisq(MNBHBH, df=1) [1] NaN > MNBHCA <- ((bBHCA - cBHCA)^2 / (bBHCA + cBHCA)) > MNBHCA [1] 9 > dchisq(MNBHCA, df=1) [1] 0.001477283 > MNBHCB <- ((bBHCB - cBHCB)^2 / (bBHCB + cBHCB)) > MNBHCB [1] 8 > dchisq(MNBHCB, df=1) [1] 0.002583373 > MNBHCC <- ((bBHCC - cBHCC)^2 / (bBHCC + cBHCC)) > MNBHCC [1] 2 > dchisq(MNBHCC, df=1) [1] 0.1037769 > MNBHCD <- ((bBHCD - cBHCD)^2 / (bBHCD + cBHCD)) > MNBHCD [1] 9 > dchisq(MNBHCD, df=1) [1] 0.001477283 > MNBHCE <- ((bBHCE - cBHCE)^2 / (bBHCE + cBHCE)) > MNBHCE [1] 9 > dchisq(MNBHCE, df=1) [1] 0.001477283 > MNBHCF <- ((bBHCF - cBHCF)^2 / (bBHCF + cBHCF)) > MNBHCF [1] 10 > dchisq(MNBHCF, df=1) [1] 0.0008500367 > MNBHCG <- ((bBHCG - cBHCG)^2 / (bBHCG + cBHCG)) > MNBHCG [1] 10 > dchisq(MNBHCG, df=1) [1] 0.0008500367 > MNBHCH <- ((bBHCH - cBHCH)^2 / (bBHCH + cBHCH)) > MNBHCH [1] 9 > dchisq(MNBHCH, df=1) [1] 0.001477283 > MNCAAA <- ((bCAAA - cCAAA)^2 / (bCAAA + cCAAA)) > MNCAAA [1] 1 > dchisq(MNCAAA, df=1) [1] 0.2419707 > MNCAAB <- ((bCAAB - cCAAB)^2 / (bCAAB + cCAAB)) > MNCAAB [1] 8 > dchisq(MNCAAB, df=1) [1] 0.002583373 > MNCAAC <- ((bCAAC - cCAAC)^2 / (bCAAC + cCAAC)) > MNCAAC [1] 9 > dchisq(MNCAAC, df=1) [1] 0.001477283 > MNCAAD <- ((bCAAD - cCAAD)^2 / (bCAAD + cCAAD)) > MNCAAD [1] 10 > dchisq(MNCAAD, df=1) [1] 0.0008500367 > MNCAAE <- ((bCAAE - cCAAE)^2 / (bCAAE + cCAAE)) > MNCAAE [1] 10 > dchisq(MNCAAE, df=1) [1] 0.0008500367 > MNCAAF <- ((bCAAF - cCAAF)^2 / (bCAAF + cCAAF)) > MNCAAF [1] 2 > dchisq(MNCAAF, df=1) [1] 0.1037769 > MNCAAG <- ((bCAAG - cCAAG)^2 / (bCAAG + cCAAG)) > MNCAAG [1] 6 > dchisq(MNCAAG, df=1) [1] 0.008108696 > MNCAAH <- ((bCAAH - cCAAH)^2 / (bCAAH + cCAAH)) > MNCAAH [1] 8 > dchisq(MNCAAH, df=1) [1] 0.002583373 > MNCABA <- ((bCABA - cCABA)^2 / (bCABA + cCABA)) > MNCABA [1] 4 > dchisq(MNCABA, df=1) [1] 0.02699548 > MNCABB <- ((bCABB - cCABB)^2 / (bCABB + cCABB)) > MNCABB [1] 9 > dchisq(MNCABB, df=1) [1] 0.001477283 > MNCABC <- ((bCABC - cCABC)^2 / (bCABC + cCABC)) > MNCABC [1] 10 > dchisq(MNCABC, df=1) [1] 0.0008500367 > MNCABD <- ((bCABD - cCABD)^2 / (bCABD + cCABD)) > MNCABD [1] 10 > dchisq(MNCABD, df=1) [1] 0.0008500367 > MNCABE <- ((bCABE - cCABE)^2 / (bCABE + cCABE)) > MNCABE [1] 10 > dchisq(MNCABE, df=1) [1] 0.0008500367 > MNCABF <- ((bCABF - cCABF)^2 / (bCABF + cCABF)) > MNCABF [1] 9 > dchisq(MNCABF, df=1) [1] 0.001477283 > MNCABG <- ((bCABG - cCABG)^2 / (bCABG + cCABG)) > MNCABG [1] 10 > dchisq(MNCABG, df=1) [1] 0.0008500367 > MNCABH <- ((bCABH - cCABH)^2 / (bCABH + cCABH)) > MNCABH [1] 9 > dchisq(MNCABH, df=1) [1] 0.001477283 > MNCACA <- ((bCACA - cCACA)^2 / (bCACA + cCACA)) > MNCACA [1] NaN > dchisq(MNCACA, df=1) [1] NaN > MNCACB <- ((bCACB - cCACB)^2 / (bCACB + cCACB)) > MNCACB [1] 7 > dchisq(MNCACB, df=1) [1] 0.004553343 > MNCACC <- ((bCACC - cCACC)^2 / (bCACC + cCACC)) > MNCACC [1] 8 > dchisq(MNCACC, df=1) [1] 0.002583373 > MNCACD <- ((bCACD - cCACD)^2 / (bCACD + cCACD)) > MNCACD [1] 10 > dchisq(MNCACD, df=1) [1] 0.0008500367 > MNCACE <- ((bCACE - cCACE)^2 / (bCACE + cCACE)) > MNCACE [1] 10 > dchisq(MNCACE, df=1) [1] 0.0008500367 > MNCACF <- ((bCACF - cCACF)^2 / (bCACF + cCACF)) > MNCACF [1] 4 > dchisq(MNCACF, df=1) [1] 0.02699548 > MNCACG <- ((bCACG - cCACG)^2 / (bCACG + cCACG)) > MNCACG [1] 6 > dchisq(MNCACG, df=1) [1] 0.008108696 > MNCACH <- ((bCACH - cCACH)^2 / (bCACH + cCACH)) > MNCACH [1] 0.2 > dchisq(MNCACH, df=1) [1] 0.8071711 > MNCBAA <- ((bCBAA - cCBAA)^2 / (bCBAA + cCBAA)) > MNCBAA [1] 7 > dchisq(MNCBAA, df=1) [1] 0.004553343 > MNCBAB <- ((bCBAB - cCBAB)^2 / (bCBAB + cCBAB)) > MNCBAB [1] 2.666667 > dchisq(MNCBAB, df=1) [1] 0.06439711 > MNCBAC <- ((bCBAC - cCBAC)^2 / (bCBAC + cCBAC)) > MNCBAC [1] 6 > dchisq(MNCBAC, df=1) [1] 0.008108696 > MNCBAD <- ((bCBAD - cCBAD)^2 / (bCBAD + cCBAD)) > MNCBAD [1] 10 > dchisq(MNCBAD, df=1) [1] 0.0008500367 > MNCBAE <- ((bCBAE - cCBAE)^2 / (bCBAE + cCBAE)) > MNCBAE [1] 10 > dchisq(MNCBAE, df=1) [1] 0.0008500367 > MNCBAF <- ((bCBAF - cCBAF)^2 / (bCBAF + cCBAF)) > MNCBAF [1] 7 > dchisq(MNCBAF, df=1) [1] 0.004553343 > MNCBAG <- ((bCBAG - cCBAG)^2 / (bCBAG + cCBAG)) > MNCBAG [1] 1 > dchisq(MNCBAG, df=1) [1] 0.2419707 > MNCBAH <- ((bCBAH - cCBAH)^2 / (bCBAH + cCBAH)) > MNCBAH [1] 2 > dchisq(MNCBAH, df=1) [1] 0.1037769 > MNCBBA <- ((bCBBA - cCBBA)^2 / (bCBBA + cCBBA)) > MNCBBA [1] 7 > dchisq(MNCBBA, df=1) [1] 0.004553343 > MNCBBB <- ((bCBBB - cCBBB)^2 / (bCBBB + cCBBB)) > MNCBBB [1] 4 > dchisq(MNCBBB, df=1) [1] 0.02699548 > MNCBBC <- ((bCBBC - cCBBC)^2 / (bCBBC + cCBBC)) > MNCBBC [1] 10 > dchisq(MNCBBC, df=1) [1] 0.0008500367 > MNCBBD <- ((bCBBD - cCBBD)^2 / (bCBBD + cCBBD)) > MNCBBD [1] 10 > dchisq(MNCBBD, df=1) [1] 0.0008500367 > MNCBBE <- ((bCBBE - cCBBE)^2 / (bCBBE + cCBBE)) > MNCBBE [1] 10 > dchisq(MNCBBE, df=1) [1] 0.0008500367 > MNCBBF <- ((bCBBF - cCBBF)^2 / (bCBBF + cCBBF)) > MNCBBF [1] 6 > dchisq(MNCBBF, df=1) [1] 0.008108696 > MNCBBG <- ((bCBBG - cCBBG)^2 / (bCBBG + cCBBG)) > MNCBBG [1] 7 > dchisq(MNCBBG, df=1) [1] 0.004553343 > MNCBBH <- ((bCBBH - cCBBH)^2 / (bCBBH + cCBBH)) > MNCBBH [1] 8 > dchisq(MNCBBH, df=1) [1] 0.002583373 > MNCBCA <- ((bCBCA - cCBCA)^2 / (bCBCA + cCBCA)) > MNCBCA [1] 7 > dchisq(MNCBCA, df=1) [1] 0.004553343 > MNCBCB <- ((bCBCB - cCBCB)^2 / (bCBCB + cCBCB)) > MNCBCB [1] NaN > dchisq(MNCBCB, df=1) [1] NaN > MNCBCC <- ((bCBCC - cCBCC)^2 / (bCBCC + cCBCC)) > MNCBCC [1] 2 > dchisq(MNCBCC, df=1) [1] 0.1037769 > MNCBCD <- ((bCBCD - cCBCD)^2 / (bCBCD + cCBCD)) > MNCBCD [1] 10 > dchisq(MNCBCD, df=1) [1] 0.0008500367 > MNCBCE <- ((bCBCE - cCBCE)^2 / (bCBCE + cCBCE)) > MNCBCE [1] 10 > dchisq(MNCBCE, df=1) [1] 0.0008500367 > MNCBCF <- ((bCBCF - cCBCF)^2 / (bCBCF + cCBCF)) > MNCBCF [1] 8 > dchisq(MNCBCF, df=1) [1] 0.002583373 > MNCBCG <- ((bCBCG - cCBCG)^2 / (bCBCG + cCBCG)) > MNCBCG [1] 9 > dchisq(MNCBCG, df=1) [1] 0.001477283 > MNCBCH <- ((bCBCH - cCBCH)^2 / (bCBCH + cCBCH)) > MNCBCH [1] 6 > dchisq(MNCBCH, df=1) [1] 0.008108696 > MNCCAA <- ((bCCAA - cCCAA)^2 / (bCCAA + cCCAA)) > MNCCAA [1] 8 > dchisq(MNCCAA, df=1) [1] 0.002583373 > MNCCAB <- ((bCCAB - cCCAB)^2 / (bCCAB + cCCAB)) > MNCCAB [1] 1.285714 > dchisq(MNCCAB, df=1) [1] 0.1849901 > MNCCAC <- ((bCCAC - cCCAC)^2 / (bCCAC + cCCAC)) > MNCCAC [1] 1 > dchisq(MNCCAC, df=1) [1] 0.2419707 > MNCCAD <- ((bCCAD - cCCAD)^2 / (bCCAD + cCCAD)) > MNCCAD [1] 10 > dchisq(MNCCAD, df=1) [1] 0.0008500367 > MNCCAE <- ((bCCAE - cCCAE)^2 / (bCCAE + cCCAE)) > MNCCAE [1] 10 > dchisq(MNCCAE, df=1) [1] 0.0008500367 > MNCCAF <- ((bCCAF - cCCAF)^2 / (bCCAF + cCCAF)) > MNCCAF [1] 8 > dchisq(MNCCAF, df=1) [1] 0.002583373 > MNCCAG <- ((bCCAG - cCCAG)^2 / (bCCAG + cCCAG)) > MNCCAG [1] 5 > dchisq(MNCCAG, df=1) [1] 0.01464498 > MNCCAH <- ((bCCAH - cCCAH)^2 / (bCCAH + cCCAH)) > MNCCAH [1] 0.3333333 > dchisq(MNCCAH, df=1) [1] 0.584909 > MNCCBA <- ((bCCBA - cCCBA)^2 / (bCCBA + cCCBA)) > MNCCBA [1] 7 > dchisq(MNCCBA, df=1) [1] 0.004553343 > MNCCBB <- ((bCCBB - cCCBB)^2 / (bCCBB + cCCBB)) > MNCCBB [1] 0 > dchisq(MNCCBB, df=1) [1] Inf > MNCCBC <- ((bCCBC - cCCBC)^2 / (bCCBC + cCCBC)) > MNCCBC [1] 7 > dchisq(MNCCBC, df=1) [1] 0.004553343 > MNCCBD <- ((bCCBD - cCCBD)^2 / (bCCBD + cCCBD)) > MNCCBD [1] 10 > dchisq(MNCCBD, df=1) [1] 0.0008500367 > MNCCBE <- ((bCCBE - cCCBE)^2 / (bCCBE + cCCBE)) > MNCCBE [1] 10 > dchisq(MNCCBE, df=1) [1] 0.0008500367 > MNCCBF <- ((bCCBF - cCCBF)^2 / (bCCBF + cCCBF)) > MNCCBF [1] 1 > dchisq(MNCCBF, df=1) [1] 0.2419707 > MNCCBG <- ((bCCBG - cCCBG)^2 / (bCCBG + cCCBG)) > MNCCBG [1] 3 > dchisq(MNCCBG, df=1) [1] 0.05139344 > MNCCBH <- ((bCCBH - cCCBH)^2 / (bCCBH + cCCBH)) > MNCCBH [1] 2 > dchisq(MNCCBH, df=1) [1] 0.1037769 > MNCCCA <- ((bCCCA - cCCCA)^2 / (bCCCA + cCCCA)) > MNCCCA [1] 8 > dchisq(MNCCCA, df=1) [1] 0.002583373 > MNCCCB <- ((bCCCB - cCCCB)^2 / (bCCCB + cCCCB)) > MNCCCB [1] 2 > dchisq(MNCCCB, df=1) [1] 0.1037769 > MNCCCC <- ((bCCCC - cCCCC)^2 / (bCCCC + cCCCC)) > MNCCCC [1] NaN > dchisq(MNCCCC, df=1) [1] NaN > MNCCCD <- ((bCCCD - cCCCD)^2 / (bCCCD + cCCCD)) > MNCCCD [1] 10 > dchisq(MNCCCD, df=1) [1] 0.0008500367 > MNCCCE <- ((bCCCE - cCCCE)^2 / (bCCCE + cCCCE)) > MNCCCE [1] 10 > dchisq(MNCCCE, df=1) [1] 0.0008500367 > MNCCCF <- ((bCCCF - cCCCF)^2 / (bCCCF + cCCCF)) > MNCCCF [1] 10 > dchisq(MNCCCF, df=1) [1] 0.0008500367 > MNCCCG <- ((bCCCG - cCCCG)^2 / (bCCCG + cCCCG)) > MNCCCG [1] 10 > dchisq(MNCCCG, df=1) [1] 0.0008500367 > MNCCCH <- ((bCCCH - cCCCH)^2 / (bCCCH + cCCCH)) > MNCCCH [1] 8 > dchisq(MNCCCH, df=1) [1] 0.002583373 > MNCDAA <- ((bCDAA - cCDAA)^2 / (bCDAA + cCDAA)) > MNCDAA [1] 10 > dchisq(MNCDAA, df=1) [1] 0.0008500367 > MNCDAB <- ((bCDAB - cCDAB)^2 / (bCDAB + cCDAB)) > MNCDAB [1] 10 > dchisq(MNCDAB, df=1) [1] 0.0008500367 > MNCDAC <- ((bCDAC - cCDAC)^2 / (bCDAC + cCDAC)) > MNCDAC [1] 9 > dchisq(MNCDAC, df=1) [1] 0.001477283 > MNCDAD <- ((bCDAD - cCDAD)^2 / (bCDAD + cCDAD)) > MNCDAD [1] 4 > dchisq(MNCDAD, df=1) [1] 0.02699548 > MNCDAE <- ((bCDAE - cCDAE)^2 / (bCDAE + cCDAE)) > MNCDAE [1] 3 > dchisq(MNCDAE, df=1) [1] 0.05139344 > MNCDAF <- ((bCDAF - cCDAF)^2 / (bCDAF + cCDAF)) > MNCDAF [1] 10 > dchisq(MNCDAF, df=1) [1] 0.0008500367 > MNCDAG <- ((bCDAG - cCDAG)^2 / (bCDAG + cCDAG)) > MNCDAG [1] 9 > dchisq(MNCDAG, df=1) [1] 0.001477283 > MNCDAH <- ((bCDAH - cCDAH)^2 / (bCDAH + cCDAH)) > MNCDAH [1] 10 > dchisq(MNCDAH, df=1) [1] 0.0008500367 > MNCDBA <- ((bCDBA - cCDBA)^2 / (bCDBA + cCDBA)) > MNCDBA [1] 10 > dchisq(MNCDBA, df=1) [1] 0.0008500367 > MNCDBB <- ((bCDBB - cCDBB)^2 / (bCDBB + cCDBB)) > MNCDBB [1] 10 > dchisq(MNCDBB, df=1) [1] 0.0008500367 > MNCDBC <- ((bCDBC - cCDBC)^2 / (bCDBC + cCDBC)) > MNCDBC [1] 5 > dchisq(MNCDBC, df=1) [1] 0.01464498 > MNCDBD <- ((bCDBD - cCDBD)^2 / (bCDBD + cCDBD)) > MNCDBD [1] 2 > dchisq(MNCDBD, df=1) [1] 0.1037769 > MNCDBE <- ((bCDBE - cCDBE)^2 / (bCDBE + cCDBE)) > MNCDBE [1] 1 > dchisq(MNCDBE, df=1) [1] 0.2419707 > MNCDBF <- ((bCDBF - cCDBF)^2 / (bCDBF + cCDBF)) > MNCDBF [1] 9 > dchisq(MNCDBF, df=1) [1] 0.001477283 > MNCDBG <- ((bCDBG - cCDBG)^2 / (bCDBG + cCDBG)) > MNCDBG [1] 9 > dchisq(MNCDBG, df=1) [1] 0.001477283 > MNCDBH <- ((bCDBH - cCDBH)^2 / (bCDBH + cCDBH)) > MNCDBH [1] 9 > dchisq(MNCDBH, df=1) [1] 0.001477283 > MNCDCA <- ((bCDCA - cCDCA)^2 / (bCDCA + cCDCA)) > MNCDCA [1] 10 > dchisq(MNCDCA, df=1) [1] 0.0008500367 > MNCDCB <- ((bCDCB - cCDCB)^2 / (bCDCB + cCDCB)) > MNCDCB [1] 10 > dchisq(MNCDCB, df=1) [1] 0.0008500367 > MNCDCC <- ((bCDCC - cCDCC)^2 / (bCDCC + cCDCC)) > MNCDCC [1] 10 > dchisq(MNCDCC, df=1) [1] 0.0008500367 > MNCDCD <- ((bCDCD - cCDCD)^2 / (bCDCD + cCDCD)) > MNCDCD [1] NaN > dchisq(MNCDCD, df=1) [1] NaN > MNCDCE <- ((bCDCE - cCDCE)^2 / (bCDCE + cCDCE)) > MNCDCE [1] 5 > dchisq(MNCDCE, df=1) [1] 0.01464498 > MNCDCF <- ((bCDCF - cCDCF)^2 / (bCDCF + cCDCF)) > MNCDCF [1] 10 > dchisq(MNCDCF, df=1) [1] 0.0008500367 > MNCDCG <- ((bCDCG - cCDCG)^2 / (bCDCG + cCDCG)) > MNCDCG [1] 10 > dchisq(MNCDCG, df=1) [1] 0.0008500367 > MNCDCH <- ((bCDCH - cCDCH)^2 / (bCDCH + cCDCH)) > MNCDCH [1] 10 > dchisq(MNCDCH, df=1) [1] 0.0008500367 > MNCEAA <- ((bCEAA - cCEAA)^2 / (bCEAA + cCEAA)) > MNCEAA [1] 10 > dchisq(MNCEAA, df=1) [1] 0.0008500367 > MNCEAB <- ((bCEAB - cCEAB)^2 / (bCEAB + cCEAB)) > MNCEAB [1] 10 > dchisq(MNCEAB, df=1) [1] 0.0008500367 > MNCEAC <- ((bCEAC - cCEAC)^2 / (bCEAC + cCEAC)) > MNCEAC [1] 9 > dchisq(MNCEAC, df=1) [1] 0.001477283 > MNCEAD <- ((bCEAD - cCEAD)^2 / (bCEAD + cCEAD)) > MNCEAD [1] 2 > dchisq(MNCEAD, df=1) [1] 0.1037769 > MNCEAE <- ((bCEAE - cCEAE)^2 / (bCEAE + cCEAE)) > MNCEAE [1] NaN > dchisq(MNCEAE, df=1) [1] NaN > MNCEAF <- ((bCEAF - cCEAF)^2 / (bCEAF + cCEAF)) > MNCEAF [1] 10 > dchisq(MNCEAF, df=1) [1] 0.0008500367 > MNCEAG <- ((bCEAG - cCEAG)^2 / (bCEAG + cCEAG)) > MNCEAG [1] 9 > dchisq(MNCEAG, df=1) [1] 0.001477283 > MNCEAH <- ((bCEAH - cCEAH)^2 / (bCEAH + cCEAH)) > MNCEAH [1] 10 > dchisq(MNCEAH, df=1) [1] 0.0008500367 > MNCEBA <- ((bCEBA - cCEBA)^2 / (bCEBA + cCEBA)) > MNCEBA [1] 10 > dchisq(MNCEBA, df=1) [1] 0.0008500367 > MNCEBB <- ((bCEBB - cCEBB)^2 / (bCEBB + cCEBB)) > MNCEBB [1] 10 > dchisq(MNCEBB, df=1) [1] 0.0008500367 > MNCEBC <- ((bCEBC - cCEBC)^2 / (bCEBC + cCEBC)) > MNCEBC [1] 4 > dchisq(MNCEBC, df=1) [1] 0.02699548 > MNCEBD <- ((bCEBD - cCEBD)^2 / (bCEBD + cCEBD)) > MNCEBD [1] 4 > dchisq(MNCEBD, df=1) [1] 0.02699548 > MNCEBE <- ((bCEBE - cCEBE)^2 / (bCEBE + cCEBE)) > MNCEBE [1] 5 > dchisq(MNCEBE, df=1) [1] 0.01464498 > MNCEBF <- ((bCEBF - cCEBF)^2 / (bCEBF + cCEBF)) > MNCEBF [1] 10 > dchisq(MNCEBF, df=1) [1] 0.0008500367 > MNCEBG <- ((bCEBG - cCEBG)^2 / (bCEBG + cCEBG)) > MNCEBG [1] 8 > dchisq(MNCEBG, df=1) [1] 0.002583373 > MNCEBH <- ((bCEBH - cCEBH)^2 / (bCEBH + cCEBH)) > MNCEBH [1] 9 > dchisq(MNCEBH, df=1) [1] 0.001477283 > MNCECA <- ((bCECA - cCECA)^2 / (bCECA + cCECA)) > MNCECA [1] 10 > dchisq(MNCECA, df=1) [1] 0.0008500367 > MNCECB <- ((bCECB - cCECB)^2 / (bCECB + cCECB)) > MNCECB [1] 10 > dchisq(MNCECB, df=1) [1] 0.0008500367 > MNCECC <- ((bCECC - cCECC)^2 / (bCECC + cCECC)) > MNCECC [1] 10 > dchisq(MNCECC, df=1) [1] 0.0008500367 > MNCECD <- ((bCECD - cCECD)^2 / (bCECD + cCECD)) > MNCECD [1] 5 > dchisq(MNCECD, df=1) [1] 0.01464498 > MNCECE <- ((bCECE - cCECE)^2 / (bCECE + cCECE)) > MNCECE [1] NaN > dchisq(MNCECE, df=1) [1] NaN > MNCECF <- ((bCECF - cCECF)^2 / (bCECF + cCECF)) > MNCECF [1] 10 > dchisq(MNCECF, df=1) [1] 0.0008500367 > MNCECG <- ((bCECG - cCECG)^2 / (bCECG + cCECG)) > MNCECG [1] 10 > dchisq(MNCECG, df=1) [1] 0.0008500367 > MNCECH <- ((bCECH - cCECH)^2 / (bCECH + cCECH)) > MNCECH [1] 10 > dchisq(MNCECH, df=1) [1] 0.0008500367 > MNCFAA <- ((bCFAA - cCFAA)^2 / (bCFAA + cCFAA)) > MNCFAA [1] 5 > dchisq(MNCFAA, df=1) [1] 0.01464498 > MNCFAB <- ((bCFAB - cCFAB)^2 / (bCFAB + cCFAB)) > MNCFAB [1] 8 > dchisq(MNCFAB, df=1) [1] 0.002583373 > MNCFAC <- ((bCFAC - cCFAC)^2 / (bCFAC + cCFAC)) > MNCFAC [1] 9 > dchisq(MNCFAC, df=1) [1] 0.001477283 > MNCFAD <- ((bCFAD - cCFAD)^2 / (bCFAD + cCFAD)) > MNCFAD [1] 10 > dchisq(MNCFAD, df=1) [1] 0.0008500367 > MNCFAE <- ((bCFAE - cCFAE)^2 / (bCFAE + cCFAE)) > MNCFAE [1] 10 > dchisq(MNCFAE, df=1) [1] 0.0008500367 > MNCFAF <- ((bCFAF - cCFAF)^2 / (bCFAF + cCFAF)) > MNCFAF [1] 2 > dchisq(MNCFAF, df=1) [1] 0.1037769 > MNCFAG <- ((bCFAG - cCFAG)^2 / (bCFAG + cCFAG)) > MNCFAG [1] 8 > dchisq(MNCFAG, df=1) [1] 0.002583373 > MNCFAH <- ((bCFAH - cCFAH)^2 / (bCFAH + cCFAH)) > MNCFAH [1] 8 > dchisq(MNCFAH, df=1) [1] 0.002583373 > MNCFBA <- ((bCFBA - cCFBA)^2 / (bCFBA + cCFBA)) > MNCFBA [1] 7 > dchisq(MNCFBA, df=1) [1] 0.004553343 > MNCFBB <- ((bCFBB - cCFBB)^2 / (bCFBB + cCFBB)) > MNCFBB [1] 9 > dchisq(MNCFBB, df=1) [1] 0.001477283 > MNCFBC <- ((bCFBC - cCFBC)^2 / (bCFBC + cCFBC)) > MNCFBC [1] 10 > dchisq(MNCFBC, df=1) [1] 0.0008500367 > MNCFBD <- ((bCFBD - cCFBD)^2 / (bCFBD + cCFBD)) > MNCFBD [1] 10 > dchisq(MNCFBD, df=1) [1] 0.0008500367 > MNCFBE <- ((bCFBE - cCFBE)^2 / (bCFBE + cCFBE)) > MNCFBE [1] 10 > dchisq(MNCFBE, df=1) [1] 0.0008500367 > MNCFBF <- ((bCFBF - cCFBF)^2 / (bCFBF + cCFBF)) > MNCFBF [1] 9 > dchisq(MNCFBF, df=1) [1] 0.001477283 > MNCFBG <- ((bCFBG - cCFBG)^2 / (bCFBG + cCFBG)) > MNCFBG [1] 10 > dchisq(MNCFBG, df=1) [1] 0.0008500367 > MNCFBH <- ((bCFBH - cCFBH)^2 / (bCFBH + cCFBH)) > MNCFBH [1] 10 > dchisq(MNCFBH, df=1) [1] 0.0008500367 > MNCFCA <- ((bCFCA - cCFCA)^2 / (bCFCA + cCFCA)) > MNCFCA [1] 4 > dchisq(MNCFCA, df=1) [1] 0.02699548 > MNCFCB <- ((bCFCB - cCFCB)^2 / (bCFCB + cCFCB)) > MNCFCB [1] 8 > dchisq(MNCFCB, df=1) [1] 0.002583373 > MNCFCC <- ((bCFCC - cCFCC)^2 / (bCFCC + cCFCC)) > MNCFCC [1] 10 > dchisq(MNCFCC, df=1) [1] 0.0008500367 > MNCFCD <- ((bCFCD - cCFCD)^2 / (bCFCD + cCFCD)) > MNCFCD [1] 10 > dchisq(MNCFCD, df=1) [1] 0.0008500367 > MNCFCE <- ((bCFCE - cCFCE)^2 / (bCFCE + cCFCE)) > MNCFCE [1] 10 > dchisq(MNCFCE, df=1) [1] 0.0008500367 > MNCFCF <- ((bCFCF - cCFCF)^2 / (bCFCF + cCFCF)) > MNCFCF [1] NaN > dchisq(MNCFCF, df=1) [1] NaN > MNCFCG <- ((bCFCG - cCFCG)^2 / (bCFCG + cCFCG)) > MNCFCG [1] 2 > dchisq(MNCFCG, df=1) [1] 0.1037769 > MNCFCH <- ((bCFCH - cCFCH)^2 / (bCFCH + cCFCH)) > MNCFCH [1] 1.8 > dchisq(MNCFCH, df=1) [1] 0.1208951 > MNCGAA <- ((bCGAA - cCGAA)^2 / (bCGAA + cCGAA)) > MNCGAA [1] 7 > dchisq(MNCGAA, df=1) [1] 0.004553343 > MNCGAB <- ((bCGAB - cCGAB)^2 / (bCGAB + cCGAB)) > MNCGAB [1] 9 > dchisq(MNCGAB, df=1) [1] 0.001477283 > MNCGAC <- ((bCGAC - cCGAC)^2 / (bCGAC + cCGAC)) > MNCGAC [1] 10 > dchisq(MNCGAC, df=1) [1] 0.0008500367 > MNCGAD <- ((bCGAD - cCGAD)^2 / (bCGAD + cCGAD)) > MNCGAD [1] 10 > dchisq(MNCGAD, df=1) [1] 0.0008500367 > MNCGAE <- ((bCGAE - cCGAE)^2 / (bCGAE + cCGAE)) > MNCGAE [1] 10 > dchisq(MNCGAE, df=1) [1] 0.0008500367 > MNCGAF <- ((bCGAF - cCGAF)^2 / (bCGAF + cCGAF)) > MNCGAF [1] 5 > dchisq(MNCGAF, df=1) [1] 0.01464498 > MNCGAG <- ((bCGAG - cCGAG)^2 / (bCGAG + cCGAG)) > MNCGAG [1] 9 > dchisq(MNCGAG, df=1) [1] 0.001477283 > MNCGAH <- ((bCGAH - cCGAH)^2 / (bCGAH + cCGAH)) > MNCGAH [1] 10 > dchisq(MNCGAH, df=1) [1] 0.0008500367 > MNCGBA <- ((bCGBA - cCGBA)^2 / (bCGBA + cCGBA)) > MNCGBA [1] 8 > dchisq(MNCGBA, df=1) [1] 0.002583373 > MNCGBB <- ((bCGBB - cCGBB)^2 / (bCGBB + cCGBB)) > MNCGBB [1] 9 > dchisq(MNCGBB, df=1) [1] 0.001477283 > MNCGBC <- ((bCGBC - cCGBC)^2 / (bCGBC + cCGBC)) > MNCGBC [1] 10 > dchisq(MNCGBC, df=1) [1] 0.0008500367 > MNCGBD <- ((bCGBD - cCGBD)^2 / (bCGBD + cCGBD)) > MNCGBD [1] 10 > dchisq(MNCGBD, df=1) [1] 0.0008500367 > MNCGBE <- ((bCGBE - cCGBE)^2 / (bCGBE + cCGBE)) > MNCGBE [1] 10 > dchisq(MNCGBE, df=1) [1] 0.0008500367 > MNCGBF <- ((bCGBF - cCGBF)^2 / (bCGBF + cCGBF)) > MNCGBF [1] 10 > dchisq(MNCGBF, df=1) [1] 0.0008500367 > MNCGBG <- ((bCGBG - cCGBG)^2 / (bCGBG + cCGBG)) > MNCGBG [1] 10 > dchisq(MNCGBG, df=1) [1] 0.0008500367 > MNCGBH <- ((bCGBH - cCGBH)^2 / (bCGBH + cCGBH)) > MNCGBH [1] 10 > dchisq(MNCGBH, df=1) [1] 0.0008500367 > MNCGCA <- ((bCGCA - cCGCA)^2 / (bCGCA + cCGCA)) > MNCGCA [1] 6 > dchisq(MNCGCA, df=1) [1] 0.008108696 > MNCGCB <- ((bCGCB - cCGCB)^2 / (bCGCB + cCGCB)) > MNCGCB [1] 9 > dchisq(MNCGCB, df=1) [1] 0.001477283 > MNCGCC <- ((bCGCC - cCGCC)^2 / (bCGCC + cCGCC)) > MNCGCC [1] 10 > dchisq(MNCGCC, df=1) [1] 0.0008500367 > MNCGCD <- ((bCGCD - cCGCD)^2 / (bCGCD + cCGCD)) > MNCGCD [1] 10 > dchisq(MNCGCD, df=1) [1] 0.0008500367 > MNCGCE <- ((bCGCE - cCGCE)^2 / (bCGCE + cCGCE)) > MNCGCE [1] 10 > dchisq(MNCGCE, df=1) [1] 0.0008500367 > MNCGCF <- ((bCGCF - cCGCF)^2 / (bCGCF + cCGCF)) > MNCGCF [1] 2 > dchisq(MNCGCF, df=1) [1] 0.1037769 > MNCGCG <- ((bCGCG - cCGCG)^2 / (bCGCG + cCGCG)) > MNCGCG [1] NaN > dchisq(MNCGCG, df=1) [1] NaN > MNCGCH <- ((bCGCH - cCGCH)^2 / (bCGCH + cCGCH)) > MNCGCH [1] 6 > dchisq(MNCGCH, df=1) [1] 0.008108696 > MNCHAA <- ((bCHAA - cCHAA)^2 / (bCHAA + cCHAA)) > MNCHAA [1] 0 > dchisq(MNCHAA, df=1) [1] Inf > MNCHAB <- ((bCHAB - cCHAB)^2 / (bCHAB + cCHAB)) > MNCHAB [1] 8 > dchisq(MNCHAB, df=1) [1] 0.002583373 > MNCHAC <- ((bCHAC - cCHAC)^2 / (bCHAC + cCHAC)) > MNCHAC [1] 9 > dchisq(MNCHAC, df=1) [1] 0.001477283 > MNCHAD <- ((bCHAD - cCHAD)^2 / (bCHAD + cCHAD)) > MNCHAD [1] 10 > dchisq(MNCHAD, df=1) [1] 0.0008500367 > MNCHAE <- ((bCHAE - cCHAE)^2 / (bCHAE + cCHAE)) > MNCHAE [1] 10 > dchisq(MNCHAE, df=1) [1] 0.0008500367 > MNCHAF <- ((bCHAF - cCHAF)^2 / (bCHAF + cCHAF)) > MNCHAF [1] 1 > dchisq(MNCHAF, df=1) [1] 0.2419707 > MNCHAG <- ((bCHAG - cCHAG)^2 / (bCHAG + cCHAG)) > MNCHAG [1] 6 > dchisq(MNCHAG, df=1) [1] 0.008108696 > MNCHAH <- ((bCHAH - cCHAH)^2 / (bCHAH + cCHAH)) > MNCHAH [1] 8 > dchisq(MNCHAH, df=1) [1] 0.002583373 > MNCHBA <- ((bCHBA - cCHBA)^2 / (bCHBA + cCHBA)) > MNCHBA [1] 4 > dchisq(MNCHBA, df=1) [1] 0.02699548 > MNCHBB <- ((bCHBB - cCHBB)^2 / (bCHBB + cCHBB)) > MNCHBB [1] 9 > dchisq(MNCHBB, df=1) [1] 0.001477283 > MNCHBC <- ((bCHBC - cCHBC)^2 / (bCHBC + cCHBC)) > MNCHBC [1] 10 > dchisq(MNCHBC, df=1) [1] 0.0008500367 > MNCHBD <- ((bCHBD - cCHBD)^2 / (bCHBD + cCHBD)) > MNCHBD [1] 10 > dchisq(MNCHBD, df=1) [1] 0.0008500367 > MNCHBE <- ((bCHBE - cCHBE)^2 / (bCHBE + cCHBE)) > MNCHBE [1] 10 > dchisq(MNCHBE, df=1) [1] 0.0008500367 > MNCHBF <- ((bCHBF - cCHBF)^2 / (bCHBF + cCHBF)) > MNCHBF [1] 9 > dchisq(MNCHBF, df=1) [1] 0.001477283 > MNCHBG <- ((bCHBG - cCHBG)^2 / (bCHBG + cCHBG)) > MNCHBG [1] 10 > dchisq(MNCHBG, df=1) [1] 0.0008500367 > MNCHBH <- ((bCHBH - cCHBH)^2 / (bCHBH + cCHBH)) > MNCHBH [1] 9 > dchisq(MNCHBH, df=1) [1] 0.001477283 > MNCHCA <- ((bCHCA - cCHCA)^2 / (bCHCA + cCHCA)) > MNCHCA [1] 0.2 > dchisq(MNCHCA, df=1) [1] 0.8071711 > MNCHCB <- ((bCHCB - cCHCB)^2 / (bCHCB + cCHCB)) > MNCHCB [1] 6 > dchisq(MNCHCB, df=1) [1] 0.008108696 > MNCHCC <- ((bCHCC - cCHCC)^2 / (bCHCC + cCHCC)) > MNCHCC [1] 8 > dchisq(MNCHCC, df=1) [1] 0.002583373 > MNCHCD <- ((bCHCD - cCHCD)^2 / (bCHCD + cCHCD)) > MNCHCD [1] 10 > dchisq(MNCHCD, df=1) [1] 0.0008500367 > MNCHCE <- ((bCHCE - cCHCE)^2 / (bCHCE + cCHCE)) > MNCHCE [1] 10 > dchisq(MNCHCE, df=1) [1] 0.0008500367 > MNCHCF <- ((bCHCF - cCHCF)^2 / (bCHCF + cCHCF)) > MNCHCF [1] 1.8 > dchisq(MNCHCF, df=1) [1] 0.1208951 > MNCHCG <- ((bCHCG - cCHCG)^2 / (bCHCG + cCHCG)) > MNCHCG [1] 6 > dchisq(MNCHCG, df=1) [1] 0.008108696 > MNCHCH <- ((bCHCH - cCHCH)^2 / (bCHCH + cCHCH)) > MNCHCH [1] NaN > dchisq(MNCHCH, df=1) [1] NaN > > > > mcnemar.test(dAAAB, dABAA, correct = FALSE) McNemar's Chi-squared test data: dAAAB and dABAA McNemar's chi-squared = 8, df = 1, p-value = 0.004678 > mcnemar.test(dAAAC, dACAA, correct = FALSE) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAAAD, dADAA, correct = FALSE) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAAAE, dAEAA, correct = FALSE) recover called non-interactively; frames dumped, use debugger() to view > mcnemar.test(dAAAF, dAFAA, correct = FALSE) McNemar's Chi-squared test data: dAAAF and dAFAA McNemar's chi-squared = 2, df = 1, p-value = 0.1573 > mcnemar.test(dAAAG, dAGAA, correct = FALSE) McNemar's Chi-squared test data: dAAAG and dAGAA McNemar's chi-squared = 5, df = 1, p-value = 0.02535 > mcnemar.test(dAAAH, dAHAA, correct = FALSE) recover called non-interactively; frames dumped, use debugger() to view > > > MNAABA [1] 3 > dchisq(MNAABA, df=1) [1] 0.05139344 > mcnemar.test(dAABA, dBAAA, correct = FALSE) McNemar's Chi-squared test data: dAABA and dBAAA McNemar's chi-squared = 3, df = 1, p-value = 0.08326 > MNAACA [1] 1 > dchisq(MNAACA, df=1) [1] 0.2419707 > mcnemar.test(dAACA, dCAAA, correct = FALSE) McNemar's Chi-squared test data: dAACA and dCAAA McNemar's chi-squared = 1, df = 1, p-value = 0.3173 > MNBACA [1] 4 > dchisq(MNBACA, df=1) [1] 0.02699548 > mcnemar.test(dBACA, dCABA, correct = FALSE) McNemar's Chi-squared test data: dBACA and dCABA McNemar's chi-squared = 4, df = 1, p-value = 0.0455 > > > > summary(AA) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.5960 0.6112 0.6262 0.6361 0.6531 0.7190 > sd(AA) [1] 0.03602304 > summary(BA) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.5225 0.5940 0.5998 0.6072 0.6164 0.6970 > sd(BA) [1] 0.04340943 > summary(CA) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.5907 0.6221 0.6367 0.6433 0.6554 0.7143 > sd(CA) [1] 0.03565104 > > summary(AB) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.5048 0.5319 0.5466 0.5566 0.5674 0.6800 > sd(AB) [1] 0.05043143 > summary(BB) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.4342 0.4679 0.5033 0.5109 0.5476 0.6275 > sd(BB) [1] 0.06171646 > summary(CB) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.5130 0.5471 0.5752 0.5785 0.5885 0.6787 > sd(CB) [1] 0.04684613 > > summary(AC) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.3651 0.3918 0.4738 0.4643 0.5254 0.5802 > sd(AC) [1] 0.07600054 > summary(BC) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.2549 0.2836 0.3668 0.3567 0.4197 0.4667 > sd(BC) [1] 0.07798666 > summary(CC) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.4444 0.4567 0.5125 0.5093 0.5496 0.5913 > sd(CC) [1] 0.0556915 > > summary(AD) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1377 0.2521 0.2863 0.2784 0.3106 0.3825 > sd(AD) [1] 0.07154263 > summary(BD) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02234 0.11950 0.15820 0.15220 0.22130 0.23300 > sd(BD) [1] 0.07711158 > summary(CD) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.07198 0.18230 0.20080 0.21520 0.27270 0.36040 > sd(CD) [1] 0.08292096 > > summary(AE) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.2185 0.2388 0.2586 0.2790 0.3196 0.3666 > sd(AE) [1] 0.05461624 > summary(BE) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1043 0.1515 0.1816 0.2008 0.2557 0.3200 > sd(BE) [1] 0.07043106 > summary(CE) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.2031 0.2560 0.2672 0.2712 0.2904 0.3438 > sd(CE) [1] 0.04558539 > > summary(AF) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.5814 0.6333 0.6603 0.6589 0.6803 0.7492 > sd(AF) [1] 0.04454868 > summary(BF) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.3851 0.4249 0.4758 0.4737 0.5204 0.5821 > sd(BF) [1] 0.06526001 > summary(CF) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.5989 0.6543 0.6932 0.6808 0.7044 0.7612 > sd(CF) [1] 0.04525153 > > summary(AG) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.3861 0.5619 0.5919 0.5720 0.6141 0.6493 > sd(AG) [1] 0.07516205 > summary(BG) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.3329 0.3630 0.4396 0.4287 0.4904 0.5293 > sd(BG) [1] 0.07248483 > summary(CG) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.6583 0.6849 0.7170 0.7115 0.7359 0.7640 > sd(CG) [1] 0.03472319 > > summary(AH) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.4243 0.4848 0.5321 0.5242 0.5522 0.6088 > sd(AH) [1] 0.05857466 > summary(BH) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.3538 0.3837 0.4528 0.4445 0.4869 0.5451 > sd(BH) [1] 0.0669969 > summary(CH) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.5961 0.6155 0.6380 0.6444 0.6719 0.7078 > sd(CH) [1] 0.03648362 > > t.test(AA,AB) Welch Two Sample t-test data: AA and AB t = 4.0554, df = 16.287, p-value = 0.0008891 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.03799158 0.12096642 sample estimates: mean of x mean of y 0.636115 0.556636 > t.test(AA,AC) Welch Two Sample t-test data: AA and AC t = 6.4598, df = 12.85, p-value = 2.255e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1142825 0.2293359 sample estimates: mean of x mean of y 0.6361150 0.4643058 > t.test(AA,AD) Welch Two Sample t-test data: AA and AD t = 14.1233, df = 13.288, p-value = 2.198e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.3031388 0.4123418 sample estimates: mean of x mean of y 0.6361150 0.2783747 > t.test(AA,AE) Welch Two Sample t-test data: AA and AE t = 17.262, df = 15.584, p-value = 1.413e-11 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.3131885 0.4010989 sample estimates: mean of x mean of y 0.6361150 0.2789713 > t.test(AA,AF) Welch Two Sample t-test data: AA and AF t = -1.2557, df = 17.245, p-value = 0.2260 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.06093198 0.01543238 sample estimates: mean of x mean of y 0.6361150 0.6588648 > t.test(AA,AG) Welch Two Sample t-test data: AA and AG t = 2.4336, df = 12.927, p-value = 0.03022 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.007168617 0.121115983 sample estimates: mean of x mean of y 0.6361150 0.5719727 > t.test(AA,AH) Welch Two Sample t-test data: AA and AH t = 5.1454, df = 14.956, p-value = 0.0001208 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.06552785 0.15825035 sample estimates: mean of x mean of y 0.6361150 0.5242259 > > t.test(AB,AC) Welch Two Sample t-test data: AB and AC t = 3.2011, df = 15.639, p-value = 0.005697 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.03106992 0.15359048 sample estimates: mean of x mean of y 0.5566360 0.4643058 > t.test(AB,AD) Welch Two Sample t-test data: AB and AD t = 10.0529, df = 16.173, p-value = 2.306e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2196339 0.3368887 sample estimates: mean of x mean of y 0.5566360 0.2783747 > t.test(AB,AE) Welch Two Sample t-test data: AB and AE t = 11.8115, df = 17.887, p-value = 7.019e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2282538 0.3270756 sample estimates: mean of x mean of y 0.5566360 0.2789713 > t.test(AB,AF) Welch Two Sample t-test data: AB and AF t = -4.8042, df = 17.73, p-value = 0.0001479 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.14698295 -0.05747465 sample estimates: mean of x mean of y 0.5566360 0.6588648 > t.test(AB,AG) Welch Two Sample t-test data: AB and AG t = -0.5358, df = 15.738, p-value = 0.5996 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.07609660 0.04542320 sample estimates: mean of x mean of y 0.5566360 0.5719727 > t.test(AB,AH) Welch Two Sample t-test data: AB and AH t = 1.326, df = 17.611, p-value = 0.2018 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.01902290 0.08384310 sample estimates: mean of x mean of y 0.5566360 0.5242259 > > t.test(AC,AD) Welch Two Sample t-test data: AC and AD t = 5.6331, df = 17.935, p-value = 2.442e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1165684 0.2552938 sample estimates: mean of x mean of y 0.4643058 0.2783747 > t.test(AC,AE) Welch Two Sample t-test data: AC and AE t = 6.2622, df = 16.339, p-value = 1.030e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1227000 0.2479690 sample estimates: mean of x mean of y 0.4643058 0.2789713 > t.test(AC,AF) Welch Two Sample t-test data: AC and AF t = -6.984, df = 14.532, p-value = 5.231e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2541040 -0.1350140 sample estimates: mean of x mean of y 0.4643058 0.6588648 > t.test(AC,AG) Welch Two Sample t-test data: AC and AG t = -3.1853, df = 17.998, p-value = 0.005126 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.17868185 -0.03665195 sample estimates: mean of x mean of y 0.4643058 0.5719727 > t.test(AC,AH) Welch Two Sample t-test data: AC and AH t = -1.9747, df = 16.903, p-value = 0.06486 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.123966454 0.004126254 sample estimates: mean of x mean of y 0.4643058 0.5242259 > > t.test(AD,AE) Welch Two Sample t-test data: AD and AE t = -0.021, df = 16.831, p-value = 0.9835 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.06069383 0.05950063 sample estimates: mean of x mean of y 0.2783747 0.2789713 > t.test(AD,AF) Welch Two Sample t-test data: AD and AF t = -14.2766, df = 15.067, p-value = 3.663e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4372740 -0.3237062 sample estimates: mean of x mean of y 0.2783747 0.6588648 > t.test(AD,AG) Welch Two Sample t-test data: AD and AG t = -8.9473, df = 17.956, p-value = 4.904e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.362550 -0.224646 sample estimates: mean of x mean of y 0.2783747 0.5719727 > t.test(AD,AH) Welch Two Sample t-test data: AD and AH t = -8.4083, df = 17.325, p-value = 1.598e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3074526 -0.1842498 sample estimates: mean of x mean of y 0.2783747 0.5242259 > > t.test(AE,AF) Welch Two Sample t-test data: AE and AF t = -17.0448, df = 17.301, p-value = 2.969e-12 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4268546 -0.3329324 sample estimates: mean of x mean of y 0.2789713 0.6588648 > t.test(AE,AG) Welch Two Sample t-test data: AE and AG t = -9.9726, df = 16.432, p-value = 2.223e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3551529 -0.2308499 sample estimates: mean of x mean of y 0.2789713 0.5719727 > t.test(AE,AH) Welch Two Sample t-test data: AE and AH t = -9.684, df = 17.913, p-value = 1.528e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2984806 -0.1920286 sample estimates: mean of x mean of y 0.2789713 0.5242259 > > t.test(AF,AG) Welch Two Sample t-test data: AF and AG t = 3.1449, df = 14.629, p-value = 0.006847 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.02787065 0.14591355 sample estimates: mean of x mean of y 0.6588648 0.5719727 > t.test(AF,AH) Welch Two Sample t-test data: AF and AH t = 5.7856, df = 16.801, p-value = 2.298e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.08549633 0.18378147 sample estimates: mean of x mean of y 0.6588648 0.5242259 > > t.test(AG,AH) Welch Two Sample t-test data: AG and AH t = 1.5845, df = 16.986, p-value = 0.1315 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.01583343 0.11132703 sample estimates: mean of x mean of y 0.5719727 0.5242259 > > t.test(BA,BB) Welch Two Sample t-test data: BA and BB t = 4.0352, df = 16.154, p-value = 0.000942 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.0457382 0.1468244 sample estimates: mean of x mean of y 0.6071637 0.5108824 > t.test(BA,BC) Welch Two Sample t-test data: BA and BC t = 8.8722, df = 14.089, p-value = 3.834e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1899147 0.3109149 sample estimates: mean of x mean of y 0.6071637 0.3567489 > t.test(BA,BD) Welch Two Sample t-test data: BA and BD t = 16.2591, df = 14.184, p-value = 1.438e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.3950347 0.5149249 sample estimates: mean of x mean of y 0.6071637 0.1521839 > t.test(BA,BE) Welch Two Sample t-test data: BA and BE t = 15.5323, df = 14.975, p-value = 1.214e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.3505968 0.4621420 sample estimates: mean of x mean of y 0.6071637 0.2007943 > t.test(BA,BF) Welch Two Sample t-test data: BA and BF t = 5.3831, df = 15.66, p-value = 6.561e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.08078747 0.18605913 sample estimates: mean of x mean of y 0.6071637 0.4737404 > t.test(BA,BG) Welch Two Sample t-test data: BA and BG t = 6.6806, df = 14.72, p-value = 8.086e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1214483 0.2355327 sample estimates: mean of x mean of y 0.6071637 0.4286732 > t.test(BA,BH) Welch Two Sample t-test data: BA and BH t = 6.4416, df = 15.424, p-value = 9.714e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1089369 0.2162953 sample estimates: mean of x mean of y 0.6071637 0.4445476 > > t.test(BB,BC) Welch Two Sample t-test data: BB and BC t = 4.901, df = 17.097, p-value = 0.0001328 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.0878091 0.2204579 sample estimates: mean of x mean of y 0.5108824 0.3567489 > t.test(BB,BD) Welch Two Sample t-test data: BB and BD t = 11.4845, df = 17.176, p-value = 1.747e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2928535 0.4245435 sample estimates: mean of x mean of y 0.5108824 0.1521839 > t.test(BB,BE) Welch Two Sample t-test data: BB and BE t = 10.4713, df = 17.695, p-value = 5.218e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2477959 0.3723803 sample estimates: mean of x mean of y 0.5108824 0.2007943 > t.test(BB,BF) Welch Two Sample t-test data: BB and BF t = 1.3076, df = 17.944, p-value = 0.2075 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.02254559 0.09682959 sample estimates: mean of x mean of y 0.5108824 0.4737404 > t.test(BB,BG) Welch Two Sample t-test data: BB and BG t = 2.7308, df = 17.554, p-value = 0.01394 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.01884596 0.14557244 sample estimates: mean of x mean of y 0.5108824 0.4286732 > t.test(BB,BH) Welch Two Sample t-test data: BB and BH t = 2.3029, df = 17.88, p-value = 0.03352 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.0057878 0.1268818 sample estimates: mean of x mean of y 0.5108824 0.4445476 > > t.test(BC,BD) Welch Two Sample t-test data: BC and BD t = 5.8984, df = 17.998, p-value = 1.389e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1317010 0.2774289 sample estimates: mean of x mean of y 0.3567489 0.1521839 > t.test(BC,BE) Welch Two Sample t-test data: BC and BE t = 4.6932, df = 17.816, p-value = 0.0001859 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.08608893 0.22582027 sample estimates: mean of x mean of y 0.3567489 0.2007943 > t.test(BC,BF) Welch Two Sample t-test data: BC and BF t = -3.6381, df = 17.457, p-value = 0.001961 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.18470187 -0.04928113 sample estimates: mean of x mean of y 0.3567489 0.4737404 > t.test(BC,BG) Welch Two Sample t-test data: BC and BG t = -2.1362, df = 17.905, p-value = 0.04673 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.142687152 -0.001161448 sample estimates: mean of x mean of y 0.3567489 0.4286732 > t.test(BC,BH) Welch Two Sample t-test data: BC and BH t = -2.7005, df = 17.6, p-value = 0.01484 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.15621594 -0.01938146 sample estimates: mean of x mean of y 0.3567489 0.4445476 > > t.test(BD,BE) Welch Two Sample t-test data: BD and BE t = -1.4719, df = 17.854, p-value = 0.1585 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.11803468 0.02081392 sample estimates: mean of x mean of y 0.1521839 0.2007943 > t.test(BD,BF) Welch Two Sample t-test data: BD and BF t = -10.0658, df = 17.521, p-value = 1.050e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.388803 -0.254310 sample estimates: mean of x mean of y 0.1521839 0.4737404 > t.test(BD,BG) Welch Two Sample t-test data: BD and BG t = -8.2616, df = 17.932, p-value = 1.589e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3468196 -0.2061590 sample estimates: mean of x mean of y 0.1521839 0.4286732 > t.test(BD,BH) Welch Two Sample t-test data: BD and BH t = -9.0507, df = 17.655, p-value = 4.767e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3603246 -0.2244027 sample estimates: mean of x mean of y 0.1521839 0.4445476 > > t.test(BE,BF) Welch Two Sample t-test data: BE and BF t = -8.9893, df = 17.896, p-value = 4.705e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3367639 -0.2091283 sample estimates: mean of x mean of y 0.2007943 0.4737404 > t.test(BE,BG) Welch Two Sample t-test data: BE and BG t = -7.1301, df = 17.985, p-value = 1.218e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2950289 -0.1607289 sample estimates: mean of x mean of y 0.2007943 0.4286732 > t.test(BE,BH) Welch Two Sample t-test data: BE and BH t = -7.9297, df = 17.955, p-value = 2.827e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3083460 -0.1791606 sample estimates: mean of x mean of y 0.2007943 0.4445476 > > t.test(BF,BG) Welch Two Sample t-test data: BF and BG t = 1.4612, df = 17.805, p-value = 0.1614 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.01978245 0.10991685 sample estimates: mean of x mean of y 0.4737404 0.4286732 > t.test(BF,BH) Welch Two Sample t-test data: BF and BH t = 0.987, df = 17.988, p-value = 0.3367 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.03294734 0.09133294 sample estimates: mean of x mean of y 0.4737404 0.4445476 > > t.test(BG,BH) Welch Two Sample t-test data: BG and BH t = -0.5086, df = 17.89, p-value = 0.6173 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.08147992 0.04973112 sample estimates: mean of x mean of y 0.4286732 0.4445476 > > t.test(CA,CB) Welch Two Sample t-test data: CA and CB t = 3.4809, df = 16.806, p-value = 0.002901 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.02548997 0.10411163 sample estimates: mean of x mean of y 0.6433359 0.5785351 > t.test(CA,CC) Welch Two Sample t-test data: CA and CC t = 6.4089, df = 15.316, p-value = 1.065e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.08952406 0.17850414 sample estimates: mean of x mean of y 0.6433359 0.5093218 > t.test(CA,CD) Welch Two Sample t-test data: CA and CD t = 15, df = 12.217, p-value = 3.093e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.3660751 0.4902087 sample estimates: mean of x mean of y 0.6433359 0.2151940 > t.test(CA,CE) Welch Two Sample t-test data: CA and CE t = 20.3372, df = 17.012, p-value = 2.245e-13 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.3335701 0.4107865 sample estimates: mean of x mean of y 0.6433359 0.2711576 > t.test(CA,CF) Welch Two Sample t-test data: CA and CF t = -2.0561, df = 17.065, p-value = 0.0554 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.075879633 0.000968233 sample estimates: mean of x mean of y 0.6433359 0.6807916 > t.test(CA,CG) Welch Two Sample t-test data: CA and CG t = -4.3291, df = 17.987, p-value = 0.0004047 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.10119436 -0.03506444 sample estimates: mean of x mean of y 0.6433359 0.7114653 > t.test(CA,CH) Welch Two Sample t-test data: CA and CH t = -0.0629, df = 17.99, p-value = 0.9505 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.034906 0.032876 sample estimates: mean of x mean of y 0.6433359 0.6443509 > > t.test(CB,CC) Welch Two Sample t-test data: CB and CC t = 3.0075, df = 17.487, p-value = 0.007742 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.02076239 0.11766421 sample estimates: mean of x mean of y 0.5785351 0.5093218 > t.test(CB,CD) Welch Two Sample t-test data: CB and CD t = 12.0642, df = 14.214, p-value = 7.378e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2988373 0.4278450 sample estimates: mean of x mean of y 0.5785351 0.2151940 > t.test(CB,CE) Welch Two Sample t-test data: CB and CE t = 14.8705, df = 17.987, p-value = 1.507e-11 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2639486 0.3508064 sample estimates: mean of x mean of y 0.5785351 0.2711576 > t.test(CB,CF) Welch Two Sample t-test data: CB and CF t = -4.9647, df = 17.978, p-value = 0.0001006 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.14553238 -0.05898062 sample estimates: mean of x mean of y 0.5785351 0.6807916 > t.test(CB,CG) Welch Two Sample t-test data: CB and CG t = -7.2089, df = 16.596, p-value = 1.683e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.17190697 -0.09395343 sample estimates: mean of x mean of y 0.5785351 0.7114653 > t.test(CB,CH) Welch Two Sample t-test data: CB and CH t = -3.5052, df = 16.981, p-value = 0.002717 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.10543432 -0.02619728 sample estimates: mean of x mean of y 0.5785351 0.6443509 > > t.test(CC,CD) Welch Two Sample t-test data: CC and CD t = 9.3117, df = 15.747, p-value = 8.408e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2270785 0.3611771 sample estimates: mean of x mean of y 0.5093218 0.2151940 > t.test(CC,CE) Welch Two Sample t-test data: CC and CE t = 10.4648, df = 17.324, p-value = 6.543e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1902158 0.2861126 sample estimates: mean of x mean of y 0.5093218 0.2711576 > t.test(CC,CF) Welch Two Sample t-test data: CC and CF t = -7.5564, df = 17.276, p-value = 7.101e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2192873 -0.1236523 sample estimates: mean of x mean of y 0.5093218 0.6807916 > t.test(CC,CG) Welch Two Sample t-test data: CC and CG t = -9.74, df = 15.079, p-value = 6.738e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2463593 -0.1579277 sample estimates: mean of x mean of y 0.5093218 0.7114653 > t.test(CC,CH) Welch Two Sample t-test data: CC and CH t = -6.4135, df = 15.523, p-value = 9.906e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.17977265 -0.09028555 sample estimates: mean of x mean of y 0.5093218 0.6443509 > > t.test(CD,CE) Welch Two Sample t-test data: CD and CE t = -1.8702, df = 13.985, p-value = 0.08253 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.120148859 0.008221639 sample estimates: mean of x mean of y 0.2151940 0.2711576 > t.test(CD,CF) Welch Two Sample t-test data: CD and CF t = -15.5862, df = 13.924, p-value = 3.308e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.5297004 -0.4014949 sample estimates: mean of x mean of y 0.2151940 0.6807916 > t.test(CD,CG) Welch Two Sample t-test data: CD and CG t = -17.4571, df = 12.062, p-value = 6.296e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.5581755 -0.4343671 sample estimates: mean of x mean of y 0.2151940 0.7114653 > t.test(CD,CH) Welch Two Sample t-test data: CD and CH t = -14.9805, df = 12.359, p-value = 2.710e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4913747 -0.3669391 sample estimates: mean of x mean of y 0.2151940 0.6443509 > > t.test(CE,CF) Welch Two Sample t-test data: CE and CF t = -20.1672, df = 17.999, p-value = 8.351e-14 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4523079 -0.3669601 sample estimates: mean of x mean of y 0.2711576 0.6807916 > t.test(CE,CG) Welch Two Sample t-test data: CE and CG t = -24.2981, df = 16.813, p-value = 1.560e-14 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4785721 -0.4020433 sample estimates: mean of x mean of y 0.2711576 0.7114653 > t.test(CE,CH) Welch Two Sample t-test data: CE and CH t = -20.2123, df = 17.175, p-value = 2.056e-13 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4121180 -0.3342686 sample estimates: mean of x mean of y 0.2711576 0.6443509 > > t.test(CF,CG) Welch Two Sample t-test data: CF and CG t = -1.7006, df = 16.87, p-value = 0.1074 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.068751182 0.007403782 sample estimates: mean of x mean of y 0.6807916 0.7114653 > t.test(CF,CH) Welch Two Sample t-test data: CF and CH t = 1.9825, df = 17.225, p-value = 0.06361 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.002302078 0.075183478 sample estimates: mean of x mean of y 0.6807916 0.6443509 > > t.test(CG,CH) Welch Two Sample t-test data: CG and CH t = 4.2138, df = 17.956, p-value = 0.0005244 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.03364675 0.10058205 sample estimates: mean of x mean of y 0.7114653 0.6443509 > > #inter > > t.test(AA,BA) Welch Two Sample t-test data: AA and BA t = 1.623, df = 17.408, p-value = 0.1226 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.008617045 0.066519645 sample estimates: mean of x mean of y 0.6361150 0.6071637 > t.test(AA,CA) Welch Two Sample t-test data: AA and CA t = -0.4505, df = 17.998, p-value = 0.6577 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.04089269 0.02645089 sample estimates: mean of x mean of y 0.6361150 0.6433359 > t.test(BA,CA) Welch Two Sample t-test data: BA and CA t = -2.0363, df = 17.345, p-value = 0.05728 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.073593008 0.001248608 sample estimates: mean of x mean of y 0.6071637 0.6433359 > > t.test(AB,BB) Welch Two Sample t-test data: AB and BB t = 1.8154, df = 17.313, p-value = 0.08683 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.007348414 0.098855614 sample estimates: mean of x mean of y 0.5566360 0.5108824 > t.test(AB,CB) Welch Two Sample t-test data: AB and CB t = -1.0061, df = 17.903, p-value = 0.3278 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.06764699 0.02384879 sample estimates: mean of x mean of y 0.5566360 0.5785351 > t.test(BB,CB) Welch Two Sample t-test data: BB and CB t = -2.7611, df = 16.786, p-value = 0.01347 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.11939761 -0.01590779 sample estimates: mean of x mean of y 0.5108824 0.5785351 > > t.test(AC,BC) Welch Two Sample t-test data: AC and BC t = 3.1234, df = 17.988, p-value = 0.005874 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.03520726 0.17990654 sample estimates: mean of x mean of y 0.4643058 0.3567489 > t.test(AC,CC) Welch Two Sample t-test data: AC and CC t = -1.5108, df = 16.502, p-value = 0.1497 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.10802344 0.01799144 sample estimates: mean of x mean of y 0.4643058 0.5093218 > t.test(BC,CC) Welch Two Sample t-test data: BC and CC t = -5.0347, df = 16.285, p-value = 0.0001158 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2167238 -0.0884220 sample estimates: mean of x mean of y 0.3567489 0.5093218 > > t.test(AD,BD) Welch Two Sample t-test data: AD and BD t = 3.7937, df = 17.9, p-value = 0.001341 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.0562789 0.1961027 sample estimates: mean of x mean of y 0.2783747 0.1521839 > t.test(AD,CD) Welch Two Sample t-test data: AD and CD t = 1.8243, df = 17.622, p-value = 0.08511 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.009692024 0.136053444 sample estimates: mean of x mean of y 0.2783747 0.2151940 > t.test(BD,CD) Welch Two Sample t-test data: BD and CD t = -1.7597, df = 17.906, p-value = 0.09554 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.13826811 0.01224797 sample estimates: mean of x mean of y 0.1521839 0.2151940 > > t.test(AE,BE) Welch Two Sample t-test data: AE and BE t = 2.7738, df = 16.949, p-value = 0.01303 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.01870011 0.13765389 sample estimates: mean of x mean of y 0.2789713 0.2007943 > t.test(AE,CE) Welch Two Sample t-test data: AE and CE t = 0.3473, df = 17.442, p-value = 0.7325 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.03955841 0.05518581 sample estimates: mean of x mean of y 0.2789713 0.2711576 > t.test(BE,CE) Welch Two Sample t-test data: BE and CE t = -2.6522, df = 15.415, p-value = 0.0178 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.12677905 -0.01394755 sample estimates: mean of x mean of y 0.2007943 0.2711576 > > t.test(AF,BF) Welch Two Sample t-test data: AF and BF t = 7.4089, df = 15.891, p-value = 1.542e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1321251 0.2381237 sample estimates: mean of x mean of y 0.6588648 0.4737404 > t.test(AF,CF) Welch Two Sample t-test data: AF and CF t = -1.0919, df = 17.996, p-value = 0.2893 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.06411522 0.02026162 sample estimates: mean of x mean of y 0.6588648 0.6807916 > t.test(BF,CF) Welch Two Sample t-test data: BF and CF t = -8.2448, df = 16.029, p-value = 3.693e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2602802 -0.1538222 sample estimates: mean of x mean of y 0.4737404 0.6807916 > > t.test(AG,BG) Welch Two Sample t-test data: AG and BG t = 4.3397, df = 17.976, p-value = 0.0003958 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.07391994 0.21267906 sample estimates: mean of x mean of y 0.5719727 0.4286732 > t.test(AG,CG) Welch Two Sample t-test data: AG and CG t = -5.3278, df = 12.674, p-value = 0.0001495 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.19620376 -0.08278144 sample estimates: mean of x mean of y 0.5719727 0.7114653 > t.test(BG,CG) Welch Two Sample t-test data: BG and CG t = -11.1265, df = 12.924, p-value = 5.451e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3377330 -0.2278512 sample estimates: mean of x mean of y 0.4286732 0.7114653 > > t.test(AH,BH) Welch Two Sample t-test data: AH and BH t = 2.8313, df = 17.685, p-value = 0.01120 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.02047903 0.13887757 sample estimates: mean of x mean of y 0.5242259 0.4445476 > t.test(AH,CH) Welch Two Sample t-test data: AH and CH t = -5.5047, df = 15.07, p-value = 5.952e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.16661903 -0.07363097 sample estimates: mean of x mean of y 0.5242259 0.6443509 > t.test(BH,CH) Welch Two Sample t-test data: BH and CH t = -8.2824, df = 13.906, p-value = 9.563e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2515767 -0.1480299 sample estimates: mean of x mean of y 0.4445476 0.6443509 > > > #complete > > t.test(AA,BH) Welch Two Sample t-test data: AA and BH t = 7.9639, df = 13.802, p-value = 1.586e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1399060 0.2432288 sample estimates: mean of x mean of y 0.6361150 0.4445476 > t.test(AB,BH) Welch Two Sample t-test data: AB and BH t = 4.2269, df = 16.72, p-value = 0.0005868 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.05606949 0.16810731 sample estimates: mean of x mean of y 0.5566360 0.4445476 > t.test(AC,BH) Welch Two Sample t-test data: AC and BH t = 0.6167, df = 17.721, p-value = 0.5453 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.04762822 0.08714462 sample estimates: mean of x mean of y 0.4643058 0.4445476 > t.test(AD,BH) Welch Two Sample t-test data: AD and BH t = -5.3613, df = 17.923, p-value = 4.338e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2313112 -0.1010346 sample estimates: mean of x mean of y 0.2783747 0.4445476 > t.test(AE,BH) Welch Two Sample t-test data: AE and BH t = -6.0575, df = 17.298, p-value = 1.189e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2231707 -0.1079819 sample estimates: mean of x mean of y 0.2789713 0.4445476 > t.test(AF,BH) Welch Two Sample t-test data: AF and BH t = 8.4236, df = 15.657, p-value = 3.311e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1602855 0.2683489 sample estimates: mean of x mean of y 0.6588648 0.4445476 > t.test(AG,BH) Welch Two Sample t-test data: AG and BH t = 4.002, df = 17.767, p-value = 0.000855 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.06046862 0.19438158 sample estimates: mean of x mean of y 0.5719727 0.4445476 > > t.test(AA,BG) Welch Two Sample t-test data: AA and BG t = 8.1044, df = 13.19, p-value = 1.760e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1522252 0.2626584 sample estimates: mean of x mean of y 0.6361150 0.4286732 > t.test(AB,BG) Welch Two Sample t-test data: AB and BG t = 4.5826, df = 16.059, p-value = 0.0003038 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.06878472 0.18714088 sample estimates: mean of x mean of y 0.5566360 0.4286732 > t.test(AC,BG) Welch Two Sample t-test data: AC and BG t = 1.0729, df = 17.96, p-value = 0.2975 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.03415368 0.10541888 sample estimates: mean of x mean of y 0.4643058 0.4286732 > t.test(AD,BG) Welch Two Sample t-test data: AD and BG t = -4.6668, df = 17.997, p-value = 0.0001920 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.21796207 -0.08263493 sample estimates: mean of x mean of y 0.2783747 0.4286732 > t.test(AE,BG) Welch Two Sample t-test data: AE and BG t = -5.2161, df = 16.728, p-value = 7.359e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.21032893 -0.08907487 sample estimates: mean of x mean of y 0.2789713 0.4286732 > t.test(AF,BG) Welch Two Sample t-test data: AF and BG t = 8.5558, df = 14.95, p-value = 3.823e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1728289 0.2875543 sample estimates: mean of x mean of y 0.6588648 0.4286732 > > t.test(AA,BF) Welch Two Sample t-test data: AA and BF t = 6.8884, df = 14.019, p-value = 7.409e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1118234 0.2129258 sample estimates: mean of x mean of y 0.6361150 0.4737404 > t.test(AB,BF) Welch Two Sample t-test data: AB and BF t = 3.1784, df = 16.924, p-value = 0.005521 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.02785051 0.13794069 sample estimates: mean of x mean of y 0.5566360 0.4737404 > t.test(AC,BF) Welch Two Sample t-test data: AC and BF t = -0.2978, df = 17.598, p-value = 0.7693 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.07609681 0.05722761 sample estimates: mean of x mean of y 0.4643058 0.4737404 > t.test(AD,BF) Welch Two Sample t-test data: AD and BF t = -6.3799, df = 17.85, p-value = 5.437e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2597394 -0.1309920 sample estimates: mean of x mean of y 0.2783747 0.4737404 > t.test(AE,BF) Welch Two Sample t-test data: AE and BF t = -7.2376, df = 17.458, p-value = 1.186e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2514322 -0.1381060 sample estimates: mean of x mean of y 0.2789713 0.4737404 > > t.test(AA,BE) Welch Two Sample t-test data: AA and BE t = 17.4014, df = 13.407, p-value = 1.37e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.3814425 0.4891989 sample estimates: mean of x mean of y 0.6361150 0.2007943 > t.test(AB,BE) Welch Two Sample t-test data: AB and BE t = 12.9902, df = 16.308, p-value = 5.074e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2978597 0.4138237 sample estimates: mean of x mean of y 0.5566360 0.2007943 > t.test(AC,BE) Welch Two Sample t-test data: AC and BE t = 8.042, df = 17.897, p-value = 2.369e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1946426 0.3323804 sample estimates: mean of x mean of y 0.4643058 0.2007943 > t.test(AD,BE) Welch Two Sample t-test data: AD and BE t = 2.4437, df = 17.996, p-value = 0.02507 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.01088072 0.14428008 sample estimates: mean of x mean of y 0.2783747 0.2007943 > > t.test(AA,BD) Welch Two Sample t-test data: AA and BD t = 17.9804, df = 12.75, p-value = 1.950e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.4256697 0.5421924 sample estimates: mean of x mean of y 0.6361150 0.1521839 > t.test(AB,BD) Welch Two Sample t-test data: AB and BD t = 13.8811, df = 15.508, p-value = 3.703e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.3425253 0.4663788 sample estimates: mean of x mean of y 0.5566360 0.1521839 > t.test(AC,BD) Welch Two Sample t-test data: AC and BD t = 9.1163, df = 17.996, p-value = 3.643e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2401898 0.3840539 sample estimates: mean of x mean of y 0.4643058 0.1521839 > > t.test(AA,BC) Welch Two Sample t-test data: AA and BC t = 10.2839, df = 12.673, p-value = 1.632e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2205247 0.3382075 sample estimates: mean of x mean of y 0.6361150 0.3567489 > t.test(AB,BC) Welch Two Sample t-test data: AB and BC t = 6.8061, df = 15.407, p-value = 5.151e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1374327 0.2623415 sample estimates: mean of x mean of y 0.5566360 0.3567489 > > t.test(AA,BB) Welch Two Sample t-test data: AA and BB t = 5.5418, df = 14.495, p-value = 6.397e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.07691997 0.17354523 sample estimates: mean of x mean of y 0.6361150 0.5108824 > > t.test(AA,CH) Welch Two Sample t-test data: AA and CH t = -0.508, df = 17.997, p-value = 0.6176 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.04229919 0.02582739 sample estimates: mean of x mean of y 0.6361150 0.6443509 > t.test(AB,CH) Welch Two Sample t-test data: AB and CH t = -4.4563, df = 16.395, p-value = 0.0003764 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.12936043 -0.04606937 sample estimates: mean of x mean of y 0.5566360 0.6443509 > t.test(AC,CH) Welch Two Sample t-test data: AC and CH t = -6.7536, df = 12.939, p-value = 1.388e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2376665 -0.1224237 sample estimates: mean of x mean of y 0.4643058 0.6443509 > t.test(AD,CH) Welch Two Sample t-test data: AD and CH t = -14.411, df = 13.384, p-value = 1.555e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4206804 -0.3112720 sample estimates: mean of x mean of y 0.2783747 0.6443509 > t.test(AE,CH) Welch Two Sample t-test data: AE and CH t = -17.5916, df = 15.698, p-value = 9.445e-12 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4094792 -0.3212800 sample estimates: mean of x mean of y 0.2789713 0.6443509 > t.test(AF,CH) Welch Two Sample t-test data: AF and CH t = 0.7971, df = 17.327, p-value = 0.4362 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.02384839 0.05287619 sample estimates: mean of x mean of y 0.6588648 0.6443509 > t.test(AG,CH) Welch Two Sample t-test data: AG and CH t = -2.7395, df = 13.018, p-value = 0.01686 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.12944805 -0.01530835 sample estimates: mean of x mean of y 0.5719727 0.6443509 > > t.test(AA,CG) Welch Two Sample t-test data: AA and CG t = -4.7624, df = 17.976, p-value = 0.0001562 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.10859432 -0.04210628 sample estimates: mean of x mean of y 0.6361150 0.7114653 > t.test(AB,CG) Welch Two Sample t-test data: AB and CG t = -7.9964, df = 15.967, p-value = 5.657e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.1958826 -0.1137760 sample estimates: mean of x mean of y 0.5566360 0.7114653 > t.test(AC,CG) Welch Two Sample t-test data: AC and CG t = -9.3539, df = 12.6, p-value = 4.986e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3044276 -0.1898914 sample estimates: mean of x mean of y 0.4643058 0.7114653 > t.test(AD,CG) Welch Two Sample t-test data: AD and CG t = -17.2219, df = 13.017, p-value = 2.436e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4874115 -0.3787697 sample estimates: mean of x mean of y 0.2783747 0.7114653 > t.test(AE,CG) Welch Two Sample t-test data: AE and CG t = -21.1322, df = 15.254, p-value = 1.031e-12 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4760534 -0.3889346 sample estimates: mean of x mean of y 0.2789713 0.7114653 > t.test(AF,CG) Welch Two Sample t-test data: AF and CG t = -2.9449, df = 16.987, p-value = 0.009062 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.09028682 -0.01491418 sample estimates: mean of x mean of y 0.6588648 0.7114653 > > t.test(AA,CF) Welch Two Sample t-test data: AA and CF t = -2.4426, df = 17.138, p-value = 0.0257 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.083242073 -0.006111127 sample estimates: mean of x mean of y 0.6361150 0.6807916 > t.test(AB,CF) Welch Two Sample t-test data: AB and CF t = -5.7944, df = 17.793, p-value = 1.802e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.16920904 -0.07910216 sample estimates: mean of x mean of y 0.5566360 0.6807916 > t.test(AC,CF) Welch Two Sample t-test data: AC and CF t = -7.7396, df = 14.669, p-value = 1.491e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2762221 -0.1567495 sample estimates: mean of x mean of y 0.4643058 0.6807916 > t.test(AD,CF) Welch Two Sample t-test data: AD and CF t = -15.0327, df = 15.208, p-value = 1.547e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4594069 -0.3454269 sample estimates: mean of x mean of y 0.2783747 0.6807916 > t.test(AE,CF) Welch Two Sample t-test data: AE and CF t = -17.9152, df = 17.399, p-value = 1.185e-12 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4490591 -0.3545815 sample estimates: mean of x mean of y 0.2789713 0.6807916 > > t.test(AA,CE) Welch Two Sample t-test data: AA and CE t = 19.8637, df = 17.087, p-value = 3.031e-13 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.3262087 0.4037061 sample estimates: mean of x mean of y 0.6361150 0.2711576 > t.test(AB,CE) Welch Two Sample t-test data: AB and CE t = 13.2797, df = 17.819, p-value = 1.115e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2402813 0.3306755 sample estimates: mean of x mean of y 0.5566360 0.2711576 > t.test(AC,CE) Welch Two Sample t-test data: AC and CE t = 6.892, df = 14.734, p-value = 5.648e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1333197 0.2529767 sample estimates: mean of x mean of y 0.4643058 0.2711576 > t.test(AD,CE) Welch Two Sample t-test data: AD and CE t = 0.269, df = 15.274, p-value = 0.7915 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.04987216 0.06430636 sample estimates: mean of x mean of y 0.2783747 0.2711576 > > t.test(AA,CD) Welch Two Sample t-test data: AA and CD t = 14.723, df = 12.28, p-value = 3.601e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.3587873 0.4830547 sample estimates: mean of x mean of y 0.636115 0.215194 > t.test(AB,CD) Welch Two Sample t-test data: AB and CD t = 11.1252, df = 14.857, p-value = 1.334e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2759713 0.4069128 sample estimates: mean of x mean of y 0.556636 0.215194 > t.test(AC,CD) Welch Two Sample t-test data: AC and CD t = 7.0035, df = 17.865, p-value = 1.610e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1743423 0.3238813 sample estimates: mean of x mean of y 0.4643058 0.2151940 > > t.test(AA,CC) Welch Two Sample t-test data: AA and CC t = 6.0452, df = 15.409, p-value = 1.998e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.08219075 0.17139565 sample estimates: mean of x mean of y 0.6361150 0.5093218 > t.test(AB,CC) Welch Two Sample t-test data: AB and CC t = 1.9914, df = 17.826, p-value = 0.06198 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.002636491 0.097264891 sample estimates: mean of x mean of y 0.5566360 0.5093218 > > t.test(AA,CB) Welch Two Sample t-test data: AA and CB t = 3.0812, df = 16.886, p-value = 0.006813 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.01813251 0.09702729 sample estimates: mean of x mean of y 0.6361150 0.5785351 > > t.test(BA,CH) Welch Two Sample t-test data: BA and CH t = -2.0738, df = 17.482, p-value = 0.05316 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.0749402771 0.0005658771 sample estimates: mean of x mean of y 0.6071637 0.6443509 > t.test(BB,CH) Welch Two Sample t-test data: BB and CH t = -5.8871, df = 14.606, p-value = 3.329e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.18190557 -0.08503143 sample estimates: mean of x mean of y 0.5108824 0.6443509 > t.test(BC,CH) Welch Two Sample t-test data: BC and CH t = -10.5632, df = 12.759, p-value = 1.129e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3465349 -0.2286691 sample estimates: mean of x mean of y 0.3567489 0.6443509 > t.test(BD,CH) Welch Two Sample t-test data: BD and CH t = -18.2444, df = 12.837, p-value = 1.467e-10 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.5505212 -0.4338128 sample estimates: mean of x mean of y 0.1521839 0.6443509 > t.test(BE,CH) Welch Two Sample t-test data: BE and CH t = -17.6835, df = 13.506, p-value = 9.946e-11 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4975397 -0.3895735 sample estimates: mean of x mean of y 0.2007943 0.6443509 > t.test(BF,CH) Welch Two Sample t-test data: BF and CH t = -7.2161, df = 14.125, p-value = 4.231e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2212776 -0.1199434 sample estimates: mean of x mean of y 0.4737404 0.6443509 > t.test(BG,CH) Welch Two Sample t-test data: BG and CH t = -8.4047, df = 13.285, p-value = 1.114e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2709952 -0.1603602 sample estimates: mean of x mean of y 0.4286732 0.6443509 > > t.test(BA,CG) Welch Two Sample t-test data: BA and CG t = -5.9334, df = 17.172, p-value = 1.569e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.14136102 -0.06724218 sample estimates: mean of x mean of y 0.6071637 0.7114653 > t.test(BB,CG) Welch Two Sample t-test data: BB and CG t = -8.9573, df = 14.179, p-value = 3.25e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2485551 -0.1526107 sample estimates: mean of x mean of y 0.5108824 0.7114653 > t.test(BC,CG) Welch Two Sample t-test data: BC and CG t = -13.1398, df = 12.433, p-value = 1.169e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.4133081 -0.2961247 sample estimates: mean of x mean of y 0.3567489 0.7114653 > t.test(BD,CG) Welch Two Sample t-test data: BD and CG t = -20.9132, df = 12.506, p-value = 4.175e-11 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.6172892 -0.5012736 sample estimates: mean of x mean of y 0.1521839 0.7114653 > t.test(BE,CG) Welch Two Sample t-test data: BE and CG t = -20.5651, df = 13.131, p-value = 2.252e-11 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.5642627 -0.4570793 sample estimates: mean of x mean of y 0.2007943 0.7114653 > t.test(BF,CG) Welch Two Sample t-test data: BF and CG t = -10.1694, df = 13.718, p-value = 9.13e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2879593 -0.1874905 sample estimates: mean of x mean of y 0.4737404 0.7114653 > > t.test(BA,CF) Welch Two Sample t-test data: BA and CF t = -3.7131, df = 17.969, p-value = 0.001596 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.11529323 -0.03196257 sample estimates: mean of x mean of y 0.6071637 0.6807916 > t.test(BB,CF) Welch Two Sample t-test data: BB and CF t = -7.0209, df = 16.507, p-value = 2.426e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.2210841 -0.1187343 sample estimates: mean of x mean of y 0.5108824 0.6807916 > t.test(BC,CF) Welch Two Sample t-test data: BC and CF t = -11.3649, df = 14.443, p-value = 1.352e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.3850203 -0.2630651 sample estimates: mean of x mean of y 0.3567489 0.6807916 > t.test(BD,CF) Welch Two Sample t-test data: BD and CF t = -18.6962, df = 14.542, p-value = 1.419e-11 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.5890371 -0.4681783 sample estimates: mean of x mean of y 0.1521839 0.6807916 > t.test(BE,CF) Welch Two Sample t-test data: BE and CF t = -18.1315, df = 15.349, p-value = 8.822e-12 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.5363119 -0.4236827 sample estimates: mean of x mean of y 0.2007943 0.6807916 > > t.test(BA,CE) Welch Two Sample t-test data: BA and CE t = 16.8798, df = 17.957, p-value = 1.835e-12 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2941784 0.3778338 sample estimates: mean of x mean of y 0.6071637 0.2711576 > t.test(BB,CE) Welch Two Sample t-test data: BB and CE t = 9.8802, df = 16.568, p-value = 2.35e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1884323 0.2910173 sample estimates: mean of x mean of y 0.5108824 0.2711576 > t.test(BC,CE) Welch Two Sample t-test data: BC and CE t = 2.9963, df = 14.507, p-value = 0.009315 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.02452449 0.14665811 sample estimates: mean of x mean of y 0.3567489 0.2711576 > t.test(BD,CE) Welch Two Sample t-test data: BD and CE t = -4.2, df = 14.606, p-value = 0.0008161 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.17949361 -0.05845375 sample estimates: mean of x mean of y 0.1521839 0.2711576 > > t.test(BA,CD) Welch Two Sample t-test data: BA and CD t = 13.2432, df = 13.588, p-value = 3.751e-09 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.3283080 0.4556314 sample estimates: mean of x mean of y 0.6071637 0.2151940 > t.test(BB,CD) Welch Two Sample t-test data: BB and CD t = 9.0459, df = 16.63, p-value = 7.914e-08 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.2266064 0.3647704 sample estimates: mean of x mean of y 0.5108824 0.2151940 > t.test(BC,CD) Welch Two Sample t-test data: BC and CD t = 3.9324, df = 17.933, p-value = 0.0009823 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.06590777 0.21720205 sample estimates: mean of x mean of y 0.3567489 0.2151940 > > t.test(BA,CC) Welch Two Sample t-test data: BA and CC t = 4.3818, df = 16.988, p-value = 0.0004074 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.05072884 0.14495496 sample estimates: mean of x mean of y 0.6071637 0.5093218 > t.test(BB,CC) Welch Two Sample t-test data: BB and CC t = 0.0594, df = 17.813, p-value = 0.9533 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.05370945 0.05683065 sample estimates: mean of x mean of y 0.5108824 0.5093218 > > t.test(BA,CB) Welch Two Sample t-test data: BA and CB t = 1.4175, df = 17.897, p-value = 0.1735 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.01382002 0.07107722 sample estimates: mean of x mean of y 0.6071637 0.5785351 > > > > > dssalllm <- lm(outcome ~ model*set,data = datasubset) > dsslinlm <- lm(outcome ~ model+set,data = datasubset) > dssmodlm <- lm(outcome ~ model,data = datasubset) > dsssetlm <- lm(outcome ~ set,data = datasubset) > > dssalllmno <- lm(outcome ~ model*set -1,data = datasubset) > dsslinlmno <- lm(outcome ~ model+set -1,data = datasubset) > dssmodlmno <- lm(outcome ~ model -1,data = datasubset) > dsssetlmno <- lm(outcome ~ set -1,data = datasubset) > > summary(dssalllm) Call: lm(formula = outcome ~ model * set, data = datasubset) Residuals: Min 1Q Median 3Q Max -0.14322 -0.03855 -0.00555 0.03655 0.14523 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.2783747 0.0193083 14.417 < 2e-16 *** modelGLM -0.1261908 0.0273060 -4.621 8.80e-06 *** modelSVM -0.0631807 0.0273060 -2.314 0.02219 * setGSSS 0.0005966 0.0273060 0.022 0.98260 setNG25 0.1859311 0.0273060 6.809 2.94e-10 *** setPSBC 0.3577403 0.0273060 13.101 < 2e-16 *** setTHER 0.2782613 0.0273060 10.190 < 2e-16 *** modelGLM:setGSSS 0.0480138 0.0386165 1.243 0.21589 modelSVM:setGSSS 0.0553670 0.0386165 1.434 0.15395 modelGLM:setNG25 0.0186339 0.0386165 0.483 0.63021 modelSVM:setNG25 0.1081967 0.0386165 2.802 0.00583 ** modelGLM:setPSBC 0.0972395 0.0386165 2.518 0.01297 * modelSVM:setPSBC 0.0704016 0.0386165 1.823 0.07050 . modelGLM:setTHER 0.0804372 0.0386165 2.083 0.03914 * modelSVM:setTHER 0.0850798 0.0386165 2.203 0.02928 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06106 on 135 degrees of freedom Multiple R-Squared: 0.8933, Adjusted R-squared: 0.8822 F-statistic: 80.73 on 14 and 135 DF, p-value: < 2.2e-16 > summary(dsslinlm) Call: lm(formula = outcome ~ model + set, data = datasubset) Residuals: Min 1Q Median 3Q Max -0.169469 -0.039623 -0.005427 0.047371 0.141731 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.2408167 0.0136760 17.609 < 2e-16 *** modelGLM -0.0773259 0.0126615 -6.107 9.08e-09 *** modelSVM 0.0006283 0.0126615 0.050 0.9605 setGSSS 0.0350569 0.0163459 2.145 0.0337 * setNG25 0.2282080 0.0163459 13.961 < 2e-16 *** setPSBC 0.4136207 0.0163459 25.304 < 2e-16 *** setTHER 0.3334336 0.0163459 20.399 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06331 on 143 degrees of freedom Multiple R-Squared: 0.8785, Adjusted R-squared: 0.8734 F-statistic: 172.3 on 6 and 143 DF, p-value: < 2.2e-16 > summary(dssmodlm) Call: lm(formula = outcome ~ model, data = datasubset) Residuals: Min 1Q Median 3Q Max -0.37153 -0.16124 0.01588 0.14763 0.33140 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.4428806 0.0247867 17.868 <2e-16 *** modelGLM -0.0773259 0.0350537 -2.206 0.0289 * modelSVM 0.0006283 0.0350537 0.018 0.9857 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1753 on 147 degrees of freedom Multiple R-Squared: 0.0426, Adjusted R-squared: 0.02958 F-statistic: 3.271 on 2 and 147 DF, p-value: 0.04076 > summary(dsssetlm) Call: lm(formula = outcome ~ set, data = datasubset) Residuals: Min 1Q Median 3Q Max -0.192915 -0.035700 0.002501 0.040078 0.167297 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.21525 0.01334 16.136 <2e-16 *** setGSSS 0.03506 0.01887 1.858 0.0652 . setNG25 0.22821 0.01887 12.097 <2e-16 *** setPSBC 0.41362 0.01887 21.925 <2e-16 *** setTHER 0.33343 0.01887 17.675 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.07306 on 145 degrees of freedom Multiple R-Squared: 0.8359, Adjusted R-squared: 0.8314 F-statistic: 184.6 on 4 and 145 DF, p-value: < 2.2e-16 > > anova(dssalllm) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.2009 0.1005 26.9498 1.419e-10 *** set 4 3.9426 0.9856 264.3828 < 2.2e-16 *** model:set 8 0.0698 0.0087 2.3414 0.02181 * Residuals 135 0.5033 0.0037 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsslinlm) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.2009 0.1005 25.069 4.646e-10 *** set 4 3.9426 0.9856 245.928 < 2.2e-16 *** Residuals 143 0.5731 0.0040 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlm) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.2009 0.1005 3.2707 0.04076 * Residuals 147 4.5157 0.0307 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlm) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 4 3.9426 0.9856 184.63 < 2.2e-16 *** Residuals 145 0.7741 0.0053 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > summary(dssalllmno) Call: lm(formula = outcome ~ model * set - 1, data = datasubset) Residuals: Min 1Q Median 3Q Max -0.14322 -0.03855 -0.00555 0.03655 0.14523 Coefficients: Estimate Std. Error t value Pr(>|t|) modelANN 0.2783747 0.0193083 14.417 < 2e-16 *** modelGLM 0.1521839 0.0193083 7.882 9.61e-13 *** modelSVM 0.2151940 0.0193083 11.145 < 2e-16 *** setGSSS 0.0005966 0.0273060 0.022 0.98260 setNG25 0.1859311 0.0273060 6.809 2.94e-10 *** setPSBC 0.3577403 0.0273060 13.101 < 2e-16 *** setTHER 0.2782613 0.0273060 10.190 < 2e-16 *** modelGLM:setGSSS 0.0480138 0.0386165 1.243 0.21589 modelSVM:setGSSS 0.0553670 0.0386165 1.434 0.15395 modelGLM:setNG25 0.0186339 0.0386165 0.483 0.63021 modelSVM:setNG25 0.1081967 0.0386165 2.802 0.00583 ** modelGLM:setPSBC 0.0972395 0.0386165 2.518 0.01297 * modelSVM:setPSBC 0.0704016 0.0386165 1.823 0.07050 . modelGLM:setTHER 0.0804372 0.0386165 2.083 0.03914 * modelSVM:setTHER 0.0850798 0.0386165 2.203 0.02928 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06106 on 135 degrees of freedom Multiple R-Squared: 0.9837, Adjusted R-squared: 0.9819 F-statistic: 542.5 on 15 and 135 DF, p-value: < 2.2e-16 > summary(dsslinlmno) Call: lm(formula = outcome ~ model + set - 1, data = datasubset) Residuals: Min 1Q Median 3Q Max -0.169469 -0.039623 -0.005427 0.047371 0.141731 Coefficients: Estimate Std. Error t value Pr(>|t|) modelANN 0.24082 0.01368 17.609 <2e-16 *** modelGLM 0.16349 0.01368 11.955 <2e-16 *** modelSVM 0.24145 0.01368 17.655 <2e-16 *** setGSSS 0.03506 0.01635 2.145 0.0337 * setNG25 0.22821 0.01635 13.961 <2e-16 *** setPSBC 0.41362 0.01635 25.304 <2e-16 *** setTHER 0.33343 0.01635 20.399 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06331 on 143 degrees of freedom Multiple R-Squared: 0.9814, Adjusted R-squared: 0.9805 F-statistic: 1079 on 7 and 143 DF, p-value: < 2.2e-16 > summary(dssmodlmno) Call: lm(formula = outcome ~ model - 1, data = datasubset) Residuals: Min 1Q Median 3Q Max -0.37153 -0.16124 0.01588 0.14763 0.33140 Coefficients: Estimate Std. Error t value Pr(>|t|) modelANN 0.44288 0.02479 17.87 <2e-16 *** modelGLM 0.36555 0.02479 14.75 <2e-16 *** modelSVM 0.44351 0.02479 17.89 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1753 on 147 degrees of freedom Multiple R-Squared: 0.8536, Adjusted R-squared: 0.8506 F-statistic: 285.6 on 3 and 147 DF, p-value: < 2.2e-16 > summary(dsssetlmno) Call: lm(formula = outcome ~ set - 1, data = datasubset) Residuals: Min 1Q Median 3Q Max -0.192915 -0.035700 0.002501 0.040078 0.167297 Coefficients: Estimate Std. Error t value Pr(>|t|) setGSSF 0.21525 0.01334 16.14 <2e-16 *** setGSSS 0.25031 0.01334 18.76 <2e-16 *** setNG25 0.44346 0.01334 33.24 <2e-16 *** setPSBC 0.62887 0.01334 47.14 <2e-16 *** setTHER 0.54868 0.01334 41.13 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.07306 on 145 degrees of freedom Multiple R-Squared: 0.9749, Adjusted R-squared: 0.974 F-statistic: 1126 on 5 and 145 DF, p-value: < 2.2e-16 > > anova(dssalllmno) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 3 26.3237 8.7746 2353.6353 < 2e-16 *** set 4 3.9426 0.9856 264.3828 < 2e-16 *** model:set 8 0.0698 0.0087 2.3414 0.02181 * Residuals 135 0.5033 0.0037 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsslinlmno) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 3 26.3237 8.7746 2189.34 < 2.2e-16 *** set 4 3.9426 0.9856 245.93 < 2.2e-16 *** Residuals 143 0.5731 0.0040 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmno) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 3 26.3237 8.7746 285.64 < 2.2e-16 *** Residuals 147 4.5157 0.0307 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmno) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 5 30.0653 6.0131 1126.4 < 2.2e-16 *** Residuals 145 0.7741 0.0053 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > anova(dsslinlm,dssalllm) Analysis of Variance Table Model 1: outcome ~ model + set Model 2: outcome ~ model * set Res.Df RSS Df Sum of Sq F Pr(>F) 1 143 0.57312 2 135 0.50329 8 0.06983 2.3414 0.02181 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlm,dsslinlm) Analysis of Variance Table Model 1: outcome ~ model Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 147 4.5157 2 143 0.5731 4 3.9426 245.93 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlm,dsslinlm) Analysis of Variance Table Model 1: outcome ~ set Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 145 0.77406 2 143 0.57312 2 0.20094 25.069 4.646e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > dssalllmnoANN <- lm(outcome ~ model*set,data = datanoANN) > dsslinlmnoANN <- lm(outcome ~ model+set,data = datanoANN) > dssmodlmnoANN <- lm(outcome ~ model,data = datanoANN) > dsssetlmnoANN <- lm(outcome ~ set,data = datanoANN) > > summary(dssalllmnoANN) Call: lm(formula = outcome ~ model * set, data = datanoANN) Residuals: Min 1Q Median 3Q Max -0.143218 -0.039354 -0.007306 0.037985 0.145228 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.152184 0.019546 7.786 1.12e-11 *** modelSVM 0.063010 0.027642 2.280 0.0250 * setGSSS 0.048610 0.027642 1.759 0.0820 . setNG25 0.204565 0.027642 7.401 6.88e-11 *** setPSBC 0.454980 0.027642 16.460 < 2e-16 *** setTHER 0.358698 0.027642 12.977 < 2e-16 *** modelSVM:setGSSS 0.007353 0.039091 0.188 0.8512 modelSVM:setNG25 0.089563 0.039091 2.291 0.0243 * modelSVM:setPSBC -0.026838 0.039091 -0.687 0.4941 modelSVM:setTHER 0.004643 0.039091 0.119 0.9057 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06181 on 90 degrees of freedom Multiple R-Squared: 0.9007, Adjusted R-squared: 0.8907 F-statistic: 90.67 on 9 and 90 DF, p-value: < 2.2e-16 > summary(dsslinlmnoANN) Call: lm(formula = outcome ~ model + set, data = datanoANN) Residuals: Min 1Q Median 3Q Max -0.150690 -0.038751 -0.009047 0.046072 0.137756 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.14471 0.01562 9.263 6.77e-15 *** modelSVM 0.07795 0.01276 6.112 2.22e-08 *** setGSSS 0.05229 0.02017 2.593 0.0110 * setNG25 0.24935 0.02017 12.364 < 2e-16 *** setPSBC 0.44156 0.02017 21.895 < 2e-16 *** setTHER 0.36102 0.02017 17.901 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06378 on 94 degrees of freedom Multiple R-Squared: 0.8895, Adjusted R-squared: 0.8837 F-statistic: 151.4 on 5 and 94 DF, p-value: < 2.2e-16 > summary(dssmodlmnoANN) Call: lm(formula = outcome ~ model, data = datanoANN) Residuals: Min 1Q Median 3Q Max -0.37153 -0.17086 0.01588 0.15828 0.33140 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.36555 0.02599 14.066 <2e-16 *** modelSVM 0.07795 0.03675 2.121 0.0364 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1838 on 98 degrees of freedom Multiple R-Squared: 0.04389, Adjusted R-squared: 0.03413 F-statistic: 4.499 on 1 and 98 DF, p-value: 0.03645 > summary(dsssetlmnoANN) Call: lm(formula = outcome ~ set, data = datanoANN) Residuals: Min 1Q Median 3Q Max -0.17813 -0.05144 0.00856 0.03931 0.17673 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.18369 0.01677 10.954 <2e-16 *** setGSSS 0.05229 0.02371 2.205 0.0299 * setNG25 0.24935 0.02371 10.515 <2e-16 *** setPSBC 0.44156 0.02371 18.620 <2e-16 *** setTHER 0.36102 0.02371 15.224 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.07499 on 95 degrees of freedom Multiple R-Squared: 0.8457, Adjusted R-squared: 0.8392 F-statistic: 130.1 on 4 and 95 DF, p-value: < 2.2e-16 > > anova(dssalllmnoANN) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 1 0.15192 0.15192 39.7670 1.044e-08 *** set 4 2.92722 0.73181 191.5573 < 2.2e-16 *** model:set 4 0.03850 0.00963 2.5197 0.0466 * Residuals 90 0.34383 0.00382 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsslinlmnoANN) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 1 0.15192 0.15192 37.352 2.224e-08 *** set 4 2.92722 0.73181 179.922 < 2.2e-16 *** Residuals 94 0.38233 0.00407 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoANN) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 1 0.1519 0.1519 4.4986 0.03645 * Residuals 98 3.3096 0.0338 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoANN) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 4 2.92722 0.73181 130.13 < 2.2e-16 *** Residuals 95 0.53425 0.00562 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > anova(dsslinlmnoANN,dssalllmnoANN) Analysis of Variance Table Model 1: outcome ~ model + set Model 2: outcome ~ model * set Res.Df RSS Df Sum of Sq F Pr(>F) 1 94 0.38233 2 90 0.34383 4 0.03850 2.5197 0.0466 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoANN,dsslinlmnoANN) Analysis of Variance Table Model 1: outcome ~ model Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 98 3.3096 2 94 0.3823 4 2.9272 179.92 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoANN,dsslinlmnoANN) Analysis of Variance Table Model 1: outcome ~ set Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 95 0.53425 2 94 0.38233 1 0.15192 37.352 2.224e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > > dssalllmnoGLM <- lm(outcome ~ model*set,data = datanoGLM) > dsslinlmnoGLM <- lm(outcome ~ model+set,data = datanoGLM) > dssmodlmnoGLM <- lm(outcome ~ model,data = datanoGLM) > dsssetlmnoGLM <- lm(outcome ~ set,data = datanoGLM) > > summary(dssalllmnoGLM) Call: lm(formula = outcome ~ model * set, data = datanoGLM) Residuals: Min 1Q Median 3Q Max -0.143218 -0.034961 -0.005439 0.030423 0.145228 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.2783747 0.0182317 15.269 < 2e-16 *** modelSVM -0.0631807 0.0257835 -2.450 0.01620 * setGSSS 0.0005966 0.0257835 0.023 0.98159 setNG25 0.1859311 0.0257835 7.211 1.67e-10 *** setPSBC 0.3577403 0.0257835 13.875 < 2e-16 *** setTHER 0.2782613 0.0257835 10.792 < 2e-16 *** modelSVM:setGSSS 0.0553670 0.0364633 1.518 0.13241 modelSVM:setNG25 0.1081967 0.0364633 2.967 0.00385 ** modelSVM:setPSBC 0.0704016 0.0364633 1.931 0.05666 . modelSVM:setTHER 0.0850798 0.0364633 2.333 0.02186 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.05765 on 90 degrees of freedom Multiple R-Squared: 0.8927, Adjusted R-squared: 0.882 F-statistic: 83.23 on 9 and 90 DF, p-value: < 2.2e-16 > summary(dsslinlmnoGLM) Call: lm(formula = outcome ~ model + set, data = datanoGLM) Residuals: Min 1Q Median 3Q Max -0.175123 -0.034926 -0.003788 0.040496 0.136078 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.2464702 0.0145617 16.926 <2e-16 *** modelSVM 0.0006283 0.0118896 0.053 0.958 setGSSS 0.0282801 0.0187990 1.504 0.136 setNG25 0.2400295 0.0187990 12.768 <2e-16 *** setPSBC 0.3929411 0.0187990 20.902 <2e-16 *** setTHER 0.3208012 0.0187990 17.065 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.05945 on 94 degrees of freedom Multiple R-Squared: 0.8809, Adjusted R-squared: 0.8746 F-statistic: 139 on 5 and 94 DF, p-value: < 2.2e-16 > summary(dssmodlmnoGLM) Call: lm(formula = outcome ~ model, data = datanoGLM) Residuals: Min 1Q Median 3Q Max -0.37153 -0.15996 0.05348 0.14289 0.27611 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.4428806 0.0238580 18.563 <2e-16 *** modelSVM 0.0006283 0.0337403 0.019 0.985 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1687 on 98 degrees of freedom Multiple R-Squared: 3.539e-06, Adjusted R-squared: -0.0102 F-statistic: 0.0003468 on 1 and 98 DF, p-value: 0.9852 > summary(dsssetlmnoGLM) Call: lm(formula = outcome ~ set, data = datanoGLM) Residuals: Min 1Q Median 3Q Max -0.174808 -0.035240 -0.004102 0.040811 0.135764 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.24678 0.01322 18.663 <2e-16 *** setGSSS 0.02828 0.01870 1.512 0.134 setNG25 0.24003 0.01870 12.836 <2e-16 *** setPSBC 0.39294 0.01870 21.013 <2e-16 *** setTHER 0.32080 0.01870 17.155 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.05913 on 95 degrees of freedom Multiple R-Squared: 0.8809, Adjusted R-squared: 0.8759 F-statistic: 175.6 on 4 and 95 DF, p-value: < 2.2e-16 > > anova(dssalllmnoGLM) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 1 0.00001 0.00001 0.0030 0.95666 set 4 2.45690 0.61422 184.7882 < 2e-16 *** model:set 4 0.03305 0.00826 2.4854 0.04907 * Residuals 90 0.29915 0.00332 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsslinlmnoGLM) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 1 0.00001 0.00001 0.0028 0.958 set 4 2.45690 0.61422 173.8025 <2e-16 *** Residuals 94 0.33220 0.00353 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoGLM) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 1 0.00001 0.00001 3e-04 0.9852 Residuals 98 2.78910 0.02846 > anova(dsssetlmnoGLM) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 4 2.45690 0.61422 175.65 < 2.2e-16 *** Residuals 95 0.33221 0.00350 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > anova(dsslinlmnoGLM,dssalllmnoGLM) Analysis of Variance Table Model 1: outcome ~ model + set Model 2: outcome ~ model * set Res.Df RSS Df Sum of Sq F Pr(>F) 1 94 0.33220 2 90 0.29915 4 0.03305 2.4854 0.04907 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoGLM,dsslinlmnoGLM) Analysis of Variance Table Model 1: outcome ~ model Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 98 2.7891 2 94 0.3322 4 2.4569 173.80 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoGLM,dsslinlmnoGLM) Analysis of Variance Table Model 1: outcome ~ set Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 95 0.33221 2 94 0.33220 1 0.00001 0.0028 0.958 > > > > dssalllmnoSVM <- lm(outcome ~ model*set,data = datanoSVM) > dsslinlmnoSVM <- lm(outcome ~ model+set,data = datanoSVM) > dssmodlmnoSVM <- lm(outcome ~ model,data = datanoSVM) > dsssetlmnoSVM <- lm(outcome ~ set,data = datanoSVM) > > summary(dssalllmnoSVM) Call: lm(formula = outcome ~ model * set, data = datanoSVM) Residuals: Min 1Q Median 3Q Max -0.140673 -0.040203 -0.004892 0.037421 0.123402 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.2783747 0.0200998 13.850 < 2e-16 *** modelGLM -0.1261908 0.0284254 -4.439 2.55e-05 *** setGSSS 0.0005966 0.0284254 0.021 0.9833 setNG25 0.1859311 0.0284254 6.541 3.63e-09 *** setPSBC 0.3577403 0.0284254 12.585 < 2e-16 *** setTHER 0.2782613 0.0284254 9.789 7.83e-16 *** modelGLM:setGSSS 0.0480138 0.0401997 1.194 0.2355 modelGLM:setNG25 0.0186339 0.0401997 0.464 0.6441 modelGLM:setPSBC 0.0972395 0.0401997 2.419 0.0176 * modelGLM:setTHER 0.0804372 0.0401997 2.001 0.0484 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06356 on 90 degrees of freedom Multiple R-Squared: 0.882, Adjusted R-squared: 0.8702 F-statistic: 74.77 on 9 and 90 DF, p-value: < 2.2e-16 > summary(dsslinlmnoSVM) Call: lm(formula = outcome ~ model + set, data = datanoSVM) Residuals: Min 1Q Median 3Q Max -0.154280 -0.050377 -0.006102 0.047076 0.132355 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.25394 0.01591 15.957 < 2e-16 *** modelGLM -0.07733 0.01299 -5.951 4.56e-08 *** setGSSS 0.02460 0.02055 1.197 0.234 setNG25 0.19525 0.02055 9.503 2.09e-15 *** setPSBC 0.40636 0.02055 19.778 < 2e-16 *** setTHER 0.31848 0.02055 15.501 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06497 on 94 degrees of freedom Multiple R-Squared: 0.8713, Adjusted R-squared: 0.8644 F-statistic: 127.2 on 5 and 94 DF, p-value: < 2.2e-16 > summary(dssmodlmnoSVM) Call: lm(formula = outcome ~ model, data = datanoSVM) Residuals: Min 1Q Median 3Q Max -0.343219 -0.144969 0.006094 0.154075 0.331398 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.44288 0.02446 18.103 <2e-16 *** modelGLM -0.07733 0.03460 -2.235 0.0277 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.173 on 98 degrees of freedom Multiple R-Squared: 0.0485, Adjusted R-squared: 0.03879 F-statistic: 4.995 on 1 and 98 DF, p-value: 0.02769 > summary(dsssetlmnoSVM) Call: lm(formula = outcome ~ set, data = datanoSVM) Residuals: Min 1Q Median 3Q Max -0.1929433 -0.0412840 -0.0009382 0.0409081 0.1696707 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.21528 0.01696 12.696 < 2e-16 *** setGSSS 0.02460 0.02398 1.026 0.307 setNG25 0.19525 0.02398 8.142 1.51e-12 *** setPSBC 0.40636 0.02398 16.946 < 2e-16 *** setTHER 0.31848 0.02398 13.281 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.07583 on 95 degrees of freedom Multiple R-Squared: 0.8228, Adjusted R-squared: 0.8153 F-statistic: 110.3 on 4 and 95 DF, p-value: < 2.2e-16 > > anova(dssalllmnoSVM) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 1 0.14948 0.14948 37.0003 2.82e-08 *** set 4 2.53593 0.63398 156.9254 < 2.2e-16 *** model:set 4 0.03320 0.00830 2.0542 0.09345 . Residuals 90 0.36360 0.00404 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsslinlmnoSVM) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 1 0.14948 0.14948 35.412 4.556e-08 *** set 4 2.53593 0.63398 150.188 < 2.2e-16 *** Residuals 94 0.39680 0.00422 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoSVM) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 1 0.14948 0.14948 4.9951 0.02769 * Residuals 98 2.93273 0.02993 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoSVM) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 4 2.53593 0.63398 110.25 < 2.2e-16 *** Residuals 95 0.54628 0.00575 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > anova(dsslinlmnoSVM,dssalllmnoSVM) Analysis of Variance Table Model 1: outcome ~ model + set Model 2: outcome ~ model * set Res.Df RSS Df Sum of Sq F Pr(>F) 1 94 0.3968 2 90 0.3636 4 0.0332 2.0542 0.09345 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoSVM,dsslinlmnoSVM) Analysis of Variance Table Model 1: outcome ~ model Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 98 2.9327 2 94 0.3968 4 2.5359 150.19 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoSVM,dsslinlmnoSVM) Analysis of Variance Table Model 1: outcome ~ set Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 95 0.54628 2 94 0.39680 1 0.14948 35.412 4.556e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > > dssalllmnoPSBC <- lm(outcome ~ model*set,data = datanoPSBC) > dsslinlmnoPSBC <- lm(outcome ~ model+set,data = datanoPSBC) > dssmodlmnoPSBC <- lm(outcome ~ model,data = datanoPSBC) > dsssetlmnoPSBC <- lm(outcome ~ set,data = datanoPSBC) > > summary(dssalllmnoPSBC) Call: lm(formula = outcome ~ model * set, data = datanoPSBC) Residuals: Min 1Q Median 3Q Max -0.14322 -0.04399 -0.00555 0.04414 0.14523 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.2783747 0.0207100 13.442 < 2e-16 *** modelGLM -0.1261908 0.0292883 -4.309 3.64e-05 *** modelSVM -0.0631807 0.0292883 -2.157 0.0332 * setGSSS 0.0005966 0.0292883 0.020 0.9838 setNG25 0.1859311 0.0292883 6.348 5.25e-09 *** setTHER 0.2782613 0.0292883 9.501 6.37e-16 *** modelGLM:setGSSS 0.0480138 0.0414199 1.159 0.2489 modelSVM:setGSSS 0.0553670 0.0414199 1.337 0.1841 modelGLM:setNG25 0.0186339 0.0414199 0.450 0.6537 modelSVM:setNG25 0.1081967 0.0414199 2.612 0.0103 * modelGLM:setTHER 0.0804372 0.0414199 1.942 0.0547 . modelSVM:setTHER 0.0850798 0.0414199 2.054 0.0424 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06549 on 108 degrees of freedom Multiple R-Squared: 0.8451, Adjusted R-squared: 0.8293 F-statistic: 53.58 on 11 and 108 DF, p-value: < 2.2e-16 > summary(dsslinlmnoPSBC) Call: lm(formula = outcome ~ model + set, data = datanoPSBC) Residuals: Min 1Q Median 3Q Max -0.17240 -0.04441 -0.01014 0.05465 0.13804 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.24540 0.01504 16.315 < 2e-16 *** modelGLM -0.08942 0.01504 -5.945 3.09e-08 *** modelSVM -0.00102 0.01504 -0.068 0.9461 setGSSS 0.03506 0.01737 2.018 0.0459 * setNG25 0.22821 0.01737 13.139 < 2e-16 *** setTHER 0.33343 0.01737 19.198 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06727 on 114 degrees of freedom Multiple R-Squared: 0.8275, Adjusted R-squared: 0.82 F-statistic: 109.4 on 5 and 114 DF, p-value: < 2.2e-16 > summary(dssmodlmnoPSBC) Call: lm(formula = outcome ~ model, data = datanoPSBC) Residuals: Min 1Q Median 3Q Max -0.32158 -0.12693 -0.02873 0.14107 0.32230 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.39457 0.02437 16.189 <2e-16 *** modelGLM -0.08942 0.03447 -2.594 0.0107 * modelSVM -0.00102 0.03447 -0.030 0.9764 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1541 on 117 degrees of freedom Multiple R-Squared: 0.07049, Adjusted R-squared: 0.0546 F-statistic: 4.436 on 2 and 117 DF, p-value: 0.01390 > summary(dsssetlmnoPSBC) Call: lm(formula = outcome ~ set, data = datanoPSBC) Residuals: Min 1Q Median 3Q Max -0.192915 -0.049020 0.005054 0.055317 0.167297 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.21525 0.01445 14.896 <2e-16 *** setGSSS 0.03506 0.02044 1.715 0.089 . setNG25 0.22821 0.02044 11.167 <2e-16 *** setTHER 0.33343 0.02044 16.316 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.07915 on 116 degrees of freedom Multiple R-Squared: 0.757, Adjusted R-squared: 0.7508 F-statistic: 120.5 on 3 and 116 DF, p-value: < 2.2e-16 > > anova(dssalllmnoPSBC) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.21082 0.10541 24.5766 1.598e-09 *** set 3 2.26421 0.75474 175.9692 < 2.2e-16 *** model:set 6 0.05262 0.00877 2.0449 0.06579 . Residuals 108 0.46321 0.00429 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsslinlmnoPSBC) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.21082 0.10541 23.295 3.292e-09 *** set 3 2.26421 0.75474 166.796 < 2.2e-16 *** Residuals 114 0.51584 0.00452 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoPSBC) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.21082 0.10541 4.4362 0.01390 * Residuals 117 2.78005 0.02376 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoPSBC) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 3 2.26421 0.75474 120.48 < 2.2e-16 *** Residuals 116 0.72666 0.00626 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > anova(dsslinlmnoPSBC,dssalllmnoPSBC) Analysis of Variance Table Model 1: outcome ~ model + set Model 2: outcome ~ model * set Res.Df RSS Df Sum of Sq F Pr(>F) 1 114 0.51584 2 108 0.46321 6 0.05262 2.0449 0.06579 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoPSBC,dsslinlmnoPSBC) Analysis of Variance Table Model 1: outcome ~ model Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 117 2.78005 2 114 0.51584 3 2.26421 166.80 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoPSBC,dsslinlmnoPSBC) Analysis of Variance Table Model 1: outcome ~ set Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 116 0.72666 2 114 0.51584 2 0.21082 23.295 3.292e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > > dssalllmnoTHER <- lm(outcome ~ model*set,data = datanoTHER) > dsslinlmnoTHER <- lm(outcome ~ model+set,data = datanoTHER) > dssmodlmnoTHER <- lm(outcome ~ model,data = datanoTHER) > dsssetlmnoTHER <- lm(outcome ~ set,data = datanoTHER) > > summary(dssalllmnoTHER) Call: lm(formula = outcome ~ model * set, data = datanoTHER) Residuals: Min 1Q Median 3Q Max -0.14322 -0.03496 -0.00555 0.03978 0.14523 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.2783747 0.0198693 14.010 < 2e-16 *** modelGLM -0.1261908 0.0280994 -4.491 1.78e-05 *** modelSVM -0.0631807 0.0280994 -2.248 0.02658 * setGSSS 0.0005966 0.0280994 0.021 0.98310 setNG25 0.1859311 0.0280994 6.617 1.46e-09 *** setPSBC 0.3577403 0.0280994 12.731 < 2e-16 *** modelGLM:setGSSS 0.0480138 0.0397385 1.208 0.22959 modelSVM:setGSSS 0.0553670 0.0397385 1.393 0.16640 modelGLM:setNG25 0.0186339 0.0397385 0.469 0.64008 modelSVM:setNG25 0.1081967 0.0397385 2.723 0.00755 ** modelGLM:setPSBC 0.0972395 0.0397385 2.447 0.01602 * modelSVM:setPSBC 0.0704016 0.0397385 1.772 0.07928 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06283 on 108 degrees of freedom Multiple R-Squared: 0.8926, Adjusted R-squared: 0.8816 F-statistic: 81.57 on 11 and 108 DF, p-value: < 2.2e-16 > summary(dsslinlmnoTHER) Call: lm(formula = outcome ~ model + set, data = datanoTHER) Residuals: Min 1Q Median 3Q Max -0.168555 -0.042261 -0.005673 0.053193 0.137328 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.245220 0.014656 16.732 < 2e-16 *** modelGLM -0.085219 0.014656 -5.815 5.64e-08 *** modelSVM -0.004689 0.014656 -0.320 0.7496 setGSSS 0.035057 0.016923 2.072 0.0406 * setNG25 0.228208 0.016923 13.485 < 2e-16 *** setPSBC 0.413621 0.016923 24.441 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06554 on 114 degrees of freedom Multiple R-Squared: 0.8766, Adjusted R-squared: 0.8712 F-statistic: 162 on 5 and 114 DF, p-value: < 2.2e-16 > summary(dssmodlmnoTHER) Call: lm(formula = outcome ~ model, data = datanoTHER) Residuals: Min 1Q Median 3Q Max -0.3378 -0.1461 -0.0486 0.1811 0.3677 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.414442 0.028439 14.573 <2e-16 *** modelGLM -0.085219 0.040219 -2.119 0.0362 * modelSVM -0.004689 0.040219 -0.117 0.9074 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1799 on 117 degrees of freedom Multiple R-Squared: 0.04626, Adjusted R-squared: 0.02996 F-statistic: 2.837 on 2 and 117 DF, p-value: 0.06261 > summary(dsssetlmnoTHER) Call: lm(formula = outcome ~ set, data = datanoTHER) Residuals: Min 1Q Median 3Q Max -0.192915 -0.036096 0.003935 0.053161 0.167297 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.21525 0.01391 15.475 <2e-16 *** setGSSS 0.03506 0.01967 1.782 0.0773 . setNG25 0.22821 0.01967 11.601 <2e-16 *** setPSBC 0.41362 0.01967 21.027 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.07619 on 116 degrees of freedom Multiple R-Squared: 0.8303, Adjusted R-squared: 0.826 F-statistic: 189.2 on 3 and 116 DF, p-value: < 2.2e-16 > > anova(dssalllmnoTHER) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.1836 0.0918 23.2518 4.001e-09 *** set 3 3.2954 1.0985 278.2419 < 2.2e-16 *** model:set 6 0.0633 0.0106 2.6744 0.01852 * Residuals 108 0.4264 0.0039 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsslinlmnoTHER) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.1836 0.0918 21.369 1.314e-08 *** set 3 3.2954 1.0985 255.707 < 2.2e-16 *** Residuals 114 0.4897 0.0043 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoTHER) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.1836 0.0918 2.8374 0.06261 . Residuals 117 3.7851 0.0324 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoTHER) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 3 3.2954 1.0985 189.25 < 2.2e-16 *** Residuals 116 0.6733 0.0058 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > anova(dsslinlmnoTHER,dssalllmnoTHER) Analysis of Variance Table Model 1: outcome ~ model + set Model 2: outcome ~ model * set Res.Df RSS Df Sum of Sq F Pr(>F) 1 114 0.48972 2 108 0.42637 6 0.06335 2.6744 0.01852 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoTHER,dsslinlmnoTHER) Analysis of Variance Table Model 1: outcome ~ model Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 117 3.7851 2 114 0.4897 3 3.2954 255.71 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoTHER,dsslinlmnoTHER) Analysis of Variance Table Model 1: outcome ~ set Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 116 0.67331 2 114 0.48972 2 0.18359 21.369 1.314e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > > > > dssalllmnoNG25 <- lm(outcome ~ model*set,data = datanoNG25) > dsslinlmnoNG25 <- lm(outcome ~ model+set,data = datanoNG25) > dssmodlmnoNG25 <- lm(outcome ~ model,data = datanoNG25) > dsssetlmnoNG25 <- lm(outcome ~ set,data = datanoNG25) > > summary(dssalllmnoNG25) Call: lm(formula = outcome ~ model * set, data = datanoNG25) Residuals: Min 1Q Median 3Q Max -0.143218 -0.032335 -0.007306 0.031177 0.145228 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.2783747 0.0184756 15.067 < 2e-16 *** modelGLM -0.1261908 0.0261285 -4.830 4.54e-06 *** modelSVM -0.0631807 0.0261285 -2.418 0.01728 * setGSSS 0.0005966 0.0261285 0.023 0.98183 setPSBC 0.3577403 0.0261285 13.692 < 2e-16 *** setTHER 0.2782613 0.0261285 10.650 < 2e-16 *** modelGLM:setGSSS 0.0480138 0.0369512 1.299 0.19658 modelSVM:setGSSS 0.0553670 0.0369512 1.498 0.13695 modelGLM:setPSBC 0.0972395 0.0369512 2.632 0.00974 ** modelSVM:setPSBC 0.0704016 0.0369512 1.905 0.05941 . modelGLM:setTHER 0.0804372 0.0369512 2.177 0.03167 * modelSVM:setTHER 0.0850798 0.0369512 2.302 0.02322 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.05843 on 108 degrees of freedom Multiple R-Squared: 0.9168, Adjusted R-squared: 0.9084 F-statistic: 108.3 on 11 and 108 DF, p-value: < 2.2e-16 > summary(dsslinlmnoNG25) Call: lm(formula = outcome ~ model + set, data = datanoNG25) Residuals: Min 1Q Median 3Q Max -0.159552 -0.040340 -0.001807 0.039567 0.140552 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.24200 0.01330 18.196 < 2e-16 *** modelGLM -0.06977 0.01330 -5.246 7.26e-07 *** modelSVM -0.01047 0.01330 -0.787 0.4328 setGSSS 0.03506 0.01536 2.283 0.0243 * setPSBC 0.41362 0.01536 26.934 < 2e-16 *** setTHER 0.33343 0.01536 21.713 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.05948 on 114 degrees of freedom Multiple R-Squared: 0.909, Adjusted R-squared: 0.9051 F-statistic: 227.9 on 5 and 114 DF, p-value: < 2.2e-16 > summary(dssmodlmnoNG25) Call: lm(formula = outcome ~ model, data = datanoNG25) Residuals: Min 1Q Median 3Q Max -0.35508 -0.17261 0.00936 0.17792 0.32920 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.43752 0.03038 14.400 <2e-16 *** modelGLM -0.06977 0.04297 -1.624 0.107 modelSVM -0.01047 0.04297 -0.244 0.808 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1922 on 117 degrees of freedom Multiple R-Squared: 0.02554, Adjusted R-squared: 0.008887 F-statistic: 1.533 on 2 and 117 DF, p-value: 0.2201 > summary(dsssetlmnoNG25) Call: lm(formula = outcome ~ set, data = datanoNG25) Residuals: Min 1Q Median 3Q Max -0.192915 -0.033549 -0.001756 0.032598 0.167297 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.21525 0.01218 17.668 <2e-16 *** setGSSS 0.03506 0.01723 2.035 0.0442 * setPSBC 0.41362 0.01723 24.007 <2e-16 *** setTHER 0.33343 0.01723 19.353 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06673 on 116 degrees of freedom Multiple R-Squared: 0.8835, Adjusted R-squared: 0.8805 F-statistic: 293.2 on 3 and 116 DF, p-value: < 2.2e-16 > > anova(dssalllmnoNG25) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.1132 0.0566 16.588 5.22e-07 *** set 3 3.9169 1.3056 382.497 < 2.2e-16 *** model:set 6 0.0346 0.0058 1.690 0.1303 Residuals 108 0.3687 0.0034 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsslinlmnoNG25) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.1132 0.0566 16.007 7.466e-07 *** set 3 3.9169 1.3056 369.092 < 2.2e-16 *** Residuals 114 0.4033 0.0035 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoNG25) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.1132 0.0566 1.5335 0.2201 Residuals 117 4.3202 0.0369 > anova(dsssetlmnoNG25) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 3 3.9169 1.3056 293.22 < 2.2e-16 *** Residuals 116 0.5165 0.0045 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > anova(dsslinlmnoNG25,dssalllmnoNG25) Analysis of Variance Table Model 1: outcome ~ model + set Model 2: outcome ~ model * set Res.Df RSS Df Sum of Sq F Pr(>F) 1 114 0.40327 2 108 0.36866 6 0.03461 1.69 0.1303 > anova(dssmodlmnoNG25,dsslinlmnoNG25) Analysis of Variance Table Model 1: outcome ~ model Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 117 4.3202 2 114 0.4033 3 3.9169 369.09 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoNG25,dsslinlmnoNG25) Analysis of Variance Table Model 1: outcome ~ set Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 116 0.51652 2 114 0.40327 2 0.11325 16.007 7.466e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > > > dssalllmnoGSSF <- lm(outcome ~ model*set,data = datanoGSSF) > dsslinlmnoGSSF <- lm(outcome ~ model+set,data = datanoGSSF) > dssmodlmnoGSSF <- lm(outcome ~ model,data = datanoGSSF) > dsssetlmnoGSSF <- lm(outcome ~ set,data = datanoGSSF) > > summary(dssalllmnoGSSF) Call: lm(formula = outcome ~ model * set, data = datanoGSSF) Residuals: Min 1Q Median 3Q Max -0.101845 -0.040203 -0.007306 0.030718 0.123402 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.278971 0.017791 15.681 < 2e-16 *** modelGLM -0.078177 0.025160 -3.107 0.00241 ** modelSVM -0.007814 0.025160 -0.311 0.75673 setNG25 0.185334 0.025160 7.366 3.70e-11 *** setPSBC 0.357144 0.025160 14.195 < 2e-16 *** setTHER 0.277665 0.025160 11.036 < 2e-16 *** modelGLM:setNG25 -0.029380 0.035581 -0.826 0.41079 modelSVM:setNG25 0.052830 0.035581 1.485 0.14052 modelGLM:setPSBC 0.049226 0.035581 1.383 0.16937 modelSVM:setPSBC 0.015035 0.035581 0.423 0.67347 modelGLM:setTHER 0.032423 0.035581 0.911 0.36419 modelSVM:setTHER 0.029713 0.035581 0.835 0.40552 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.05626 on 108 degrees of freedom Multiple R-Squared: 0.8839, Adjusted R-squared: 0.8721 F-statistic: 74.75 on 11 and 108 DF, p-value: < 2.2e-16 > summary(dsslinlmnoGSSF) Call: lm(formula = outcome ~ model + set, data = datanoGSSF) Residuals: Min 1Q Median 3Q Max -0.13962 -0.03536 -0.01059 0.04130 0.12770 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.26648 0.01297 20.539 < 2e-16 *** modelGLM -0.06511 0.01297 -5.018 1.94e-06 *** modelSVM 0.01658 0.01297 1.278 0.204 setNG25 0.19315 0.01498 12.892 < 2e-16 *** setPSBC 0.37856 0.01498 25.268 < 2e-16 *** setTHER 0.29838 0.01498 19.916 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.05802 on 114 degrees of freedom Multiple R-Squared: 0.8696, Adjusted R-squared: 0.8639 F-statistic: 152.1 on 5 and 114 DF, p-value: < 2.2e-16 > summary(dssmodlmnoGSSF) Call: lm(formula = outcome ~ model, data = datanoGSSF) Residuals: Min 1Q Median 3Q Max -0.31457 -0.13149 0.04249 0.12422 0.27806 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.48401 0.02444 19.804 <2e-16 *** modelGLM -0.06511 0.03456 -1.884 0.0621 . modelSVM 0.01658 0.03456 0.480 0.6323 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1546 on 117 degrees of freedom Multiple R-Squared: 0.05066, Adjusted R-squared: 0.03443 F-statistic: 3.122 on 2 and 117 DF, p-value: 0.04777 > summary(dsssetlmnoGSSF) Call: lm(formula = outcome ~ set, data = datanoGSSF) Residuals: Min 1Q Median 3Q Max -0.188555 -0.035580 0.001383 0.035379 0.147837 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.25031 0.01238 20.23 <2e-16 *** setNG25 0.19315 0.01750 11.04 <2e-16 *** setPSBC 0.37856 0.01750 21.63 <2e-16 *** setTHER 0.29838 0.01750 17.05 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06778 on 116 degrees of freedom Multiple R-Squared: 0.819, Adjusted R-squared: 0.8143 F-statistic: 174.9 on 3 and 116 DF, p-value: < 2.2e-16 > > anova(dssalllmnoGSSF) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.14917 0.07458 23.5645 3.217e-09 *** set 3 2.41145 0.80382 253.9648 < 2.2e-16 *** model:set 6 0.04199 0.00700 2.2109 0.04736 * Residuals 108 0.34183 0.00317 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsslinlmnoGSSF) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.14917 0.07458 22.153 7.453e-09 *** set 3 2.41145 0.80382 238.749 < 2.2e-16 *** Residuals 114 0.38381 0.00337 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoGSSF) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.14917 0.07458 3.1218 0.04777 * Residuals 117 2.79527 0.02389 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoGSSF) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 3 2.41145 0.80382 174.95 < 2.2e-16 *** Residuals 116 0.53298 0.00459 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > anova(dsslinlmnoGSSF,dssalllmnoGSSF) Analysis of Variance Table Model 1: outcome ~ model + set Model 2: outcome ~ model * set Res.Df RSS Df Sum of Sq F Pr(>F) 1 114 0.38381 2 108 0.34183 6 0.04199 2.2109 0.04736 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoGSSF,dsslinlmnoGSSF) Analysis of Variance Table Model 1: outcome ~ model Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 117 2.79527 2 114 0.38381 3 2.41145 238.75 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoGSSF,dsslinlmnoGSSF) Analysis of Variance Table Model 1: outcome ~ set Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 116 0.53298 2 114 0.38381 2 0.14917 22.153 7.453e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > > dssalllmnoGSSS <- lm(outcome ~ model*set,data = datanoGSSS) > dsslinlmnoGSSS <- lm(outcome ~ model+set,data = datanoGSSS) > dssmodlmnoGSSS <- lm(outcome ~ model,data = datanoGSSS) > dsssetlmnoGSSS <- lm(outcome ~ set,data = datanoGSSS) > > summary(dssalllmnoGSSS) Call: lm(formula = outcome ~ model * set, data = datanoGSSS) Residuals: Min 1Q Median 3Q Max -0.143218 -0.037655 -0.004382 0.035096 0.145228 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.27837 0.01956 14.234 < 2e-16 *** modelGLM -0.12619 0.02766 -4.562 1.34e-05 *** modelSVM -0.06318 0.02766 -2.284 0.02431 * setNG25 0.18593 0.02766 6.722 8.76e-10 *** setPSBC 0.35774 0.02766 12.934 < 2e-16 *** setTHER 0.27826 0.02766 10.061 < 2e-16 *** modelGLM:setNG25 0.01863 0.03912 0.476 0.63476 modelSVM:setNG25 0.10820 0.03912 2.766 0.00667 ** modelGLM:setPSBC 0.09724 0.03912 2.486 0.01445 * modelSVM:setPSBC 0.07040 0.03912 1.800 0.07468 . modelGLM:setTHER 0.08044 0.03912 2.056 0.04215 * modelSVM:setTHER 0.08508 0.03912 2.175 0.03180 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06185 on 108 degrees of freedom Multiple R-Squared: 0.8834, Adjusted R-squared: 0.8715 F-statistic: 74.4 on 11 and 108 DF, p-value: < 2.2e-16 > summary(dsslinlmnoGSSS) Call: lm(formula = outcome ~ model + set, data = datanoGSSS) Residuals: Min 1Q Median 3Q Max -0.170805 -0.039715 -0.004777 0.047862 0.142506 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.240042 0.014546 16.503 < 2e-16 *** modelGLM -0.077113 0.014546 -5.301 5.69e-07 *** modelSVM 0.002739 0.014546 0.188 0.851 setNG25 0.228208 0.016796 13.587 < 2e-16 *** setPSBC 0.413621 0.016796 24.626 < 2e-16 *** setTHER 0.333434 0.016796 19.852 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06505 on 114 degrees of freedom Multiple R-Squared: 0.8639, Adjusted R-squared: 0.8579 F-statistic: 144.7 on 5 and 114 DF, p-value: < 2.2e-16 > summary(dssmodlmnoGSSS) Call: lm(formula = outcome ~ model, data = datanoGSSS) Residuals: Min 1Q Median 3Q Max -0.41462 -0.12928 0.04896 0.13159 0.29021 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.483858 0.026870 18.007 <2e-16 *** modelGLM -0.077113 0.038000 -2.029 0.0447 * modelSVM 0.002739 0.038000 0.072 0.9427 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1699 on 117 degrees of freedom Multiple R-Squared: 0.0464, Adjusted R-squared: 0.0301 F-statistic: 2.846 on 2 and 117 DF, p-value: 0.06209 > summary(dsssetlmnoGSSS) Call: lm(formula = outcome ~ set, data = datanoGSSS) Residuals: Min 1Q Median 3Q Max -0.1929149 -0.0355803 0.0004998 0.0372390 0.1672971 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.21525 0.01363 15.79 <2e-16 *** setNG25 0.22821 0.01928 11.84 <2e-16 *** setPSBC 0.41362 0.01928 21.45 <2e-16 *** setTHER 0.33343 0.01928 17.29 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.07467 on 116 degrees of freedom Multiple R-Squared: 0.8175, Adjusted R-squared: 0.8127 F-statistic: 173.2 on 3 and 116 DF, p-value: < 2.2e-16 > > anova(dssalllmnoGSSS) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.16440 0.08220 21.4908 1.39e-08 *** set 3 2.89664 0.96555 252.4318 < 2.2e-16 *** model:set 6 0.06929 0.01155 3.0192 0.009097 ** Residuals 108 0.41310 0.00382 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsslinlmnoGSSS) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.16440 0.08220 19.426 5.496e-08 *** set 3 2.89664 0.96555 228.182 < 2.2e-16 *** Residuals 114 0.48239 0.00423 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoGSSS) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 2 0.1644 0.0822 2.8463 0.06209 . Residuals 117 3.3790 0.0289 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoGSSS) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 3 2.89664 0.96555 173.17 < 2.2e-16 *** Residuals 116 0.64679 0.00558 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > anova(dsslinlmnoGSSS,dssalllmnoGSSS) Analysis of Variance Table Model 1: outcome ~ model + set Model 2: outcome ~ model * set Res.Df RSS Df Sum of Sq F Pr(>F) 1 114 0.48239 2 108 0.41310 6 0.06929 3.0192 0.009097 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dssmodlmnoGSSS,dsslinlmnoGSSS) Analysis of Variance Table Model 1: outcome ~ model Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 117 3.3790 2 114 0.4824 3 2.8966 228.18 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(dsssetlmnoGSSS,dsslinlmnoGSSS) Analysis of Variance Table Model 1: outcome ~ set Model 2: outcome ~ model + set Res.Df RSS Df Sum of Sq F Pr(>F) 1 116 0.64679 2 114 0.48239 2 0.16440 19.426 5.496e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > > > > > > > > > datamodelA <- data[c(1:10,31:40,61:70,91:100,121:130),1:3] > datamodelB <- data[c(11:20,41:50,71:80,101:110,131:140),1:3] > datamodelC <- data[c(21:30,51:60,81:90,111:120,141:150),1:3] > > datasetA <- data[c(1:30),1:3] > datasetB <- data[c(31:60),1:3] > datasetC <- data[c(61:90),1:3] > datasetD <- data[c(91:120),1:3] > datasetE <- data[c(121:150),1:3] > > > dmAalllm <- lm(outcome ~ set -1,data = datamodelA) > dmBalllm <- lm(outcome ~ set -1,data = datamodelB) > dmCalllm <- lm(outcome ~ set -1,data = datamodelC) > > dsAalllm <- lm(outcome ~ model -1,data = datasetA) > dsBalllm <- lm(outcome ~ model -1,data = datasetB) > dsCalllm <- lm(outcome ~ model -1,data = datasetC) > dsDalllm <- lm(outcome ~ model -1,data = datasetD) > dsEalllm <- lm(outcome ~ model -1,data = datasetE) > > summary(dmAalllm) Call: lm(formula = outcome ~ set - 1, data = datamodelA) Residuals: Min 1Q Median 3Q Max -0.140673 -0.034020 -0.004701 0.031030 0.123402 Coefficients: Estimate Std. Error t value Pr(>|t|) setGSSF 0.27837 0.01882 14.79 <2e-16 *** setGSSS 0.27897 0.01882 14.82 <2e-16 *** setNG25 0.46431 0.01882 24.66 <2e-16 *** setPSBC 0.63612 0.01882 33.79 <2e-16 *** setTHER 0.55664 0.01882 29.57 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.05953 on 45 degrees of freedom Multiple R-Squared: 0.9855, Adjusted R-squared: 0.9839 F-statistic: 612.6 on 5 and 45 DF, p-value: < 2.2e-16 > anova(dmAalllm) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 5 10.8538 2.1708 612.58 < 2.2e-16 *** Residuals 45 0.1595 0.0035 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(dmBalllm) Call: lm(formula = outcome ~ set - 1, data = datamodelB) Residuals: Min 1Q Median 3Q Max -0.12985 -0.04299 -0.00782 0.05059 0.11922 Coefficients: Estimate Std. Error t value Pr(>|t|) setGSSF 0.1522 0.0213 7.145 6.21e-09 *** setGSSS 0.2008 0.0213 9.427 3.19e-12 *** setNG25 0.3568 0.0213 16.750 < 2e-16 *** setPSBC 0.6072 0.0213 28.507 < 2e-16 *** setTHER 0.5109 0.0213 23.986 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.06735 on 45 degrees of freedom Multiple R-Squared: 0.9757, Adjusted R-squared: 0.973 F-statistic: 361.7 on 5 and 45 DF, p-value: < 2.2e-16 > anova(dmBalllm) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 5 8.2040 1.6408 361.70 < 2.2e-16 *** Residuals 45 0.2041 0.0045 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(dmCalllm) Call: lm(formula = outcome ~ set - 1, data = datamodelC) Residuals: Min 1Q Median 3Q Max -0.143218 -0.034538 -0.007306 0.027023 0.145228 Coefficients: Estimate Std. Error t value Pr(>|t|) setGSSF 0.21519 0.01762 12.21 6.92e-16 *** setGSSS 0.27116 0.01762 15.39 < 2e-16 *** setNG25 0.50932 0.01762 28.91 < 2e-16 *** setPSBC 0.64334 0.01762 36.51 < 2e-16 *** setTHER 0.57854 0.01762 32.84 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.05572 on 45 degrees of freedom Multiple R-Squared: 0.9878, Adjusted R-squared: 0.9864 F-statistic: 726.6 on 5 and 45 DF, p-value: < 2.2e-16 > anova(dmCalllm) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) set 5 11.2783 2.2557 726.65 < 2.2e-16 *** Residuals 45 0.1397 0.0031 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > summary(dsAalllm) Call: lm(formula = outcome ~ model - 1, data = datasetA) Residuals: Min 1Q Median 3Q Max -0.084633 -0.020670 -0.007391 0.014965 0.089789 Coefficients: Estimate Std. Error t value Pr(>|t|) modelANN 0.63612 0.01218 52.21 <2e-16 *** modelGLM 0.60716 0.01218 49.84 <2e-16 *** modelSVM 0.64334 0.01218 52.80 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.03853 on 27 degrees of freedom Multiple R-Squared: 0.9966, Adjusted R-squared: 0.9963 F-statistic: 2666 on 3 and 27 DF, p-value: < 2.2e-16 > anova(dsAalllm) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 3 11.8717 3.9572 2666 < 2.2e-16 *** Residuals 27 0.0401 0.0015 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(dsBalllm) Call: lm(formula = outcome ~ model - 1, data = datasetB) Residuals: Min 1Q Median 3Q Max -0.076661 -0.040073 -0.006649 0.025157 0.123402 Coefficients: Estimate Std. Error t value Pr(>|t|) modelANN 0.55664 0.01688 32.98 <2e-16 *** modelGLM 0.51088 0.01688 30.27 <2e-16 *** modelSVM 0.57854 0.01688 34.28 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.05338 on 27 degrees of freedom Multiple R-Squared: 0.9916, Adjusted R-squared: 0.9906 F-statistic: 1060 on 3 and 27 DF, p-value: < 2.2e-16 > anova(dsBalllm) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 3 9.0555 3.0185 1059.5 < 2.2e-16 *** Residuals 27 0.0769 0.0028 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(dsCalllm) Call: lm(formula = outcome ~ model - 1, data = datasetC) Residuals: Min 1Q Median 3Q Max -0.10184 -0.06187 0.01004 0.06107 0.11589 Coefficients: Estimate Std. Error t value Pr(>|t|) modelANN 0.46431 0.02233 20.79 < 2e-16 *** modelGLM 0.35675 0.02233 15.98 2.77e-15 *** modelSVM 0.50932 0.02233 22.81 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.07062 on 27 degrees of freedom Multiple R-Squared: 0.9781, Adjusted R-squared: 0.9757 F-statistic: 402.6 on 3 and 27 DF, p-value: < 2.2e-16 > anova(dsCalllm) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 3 6.0226 2.0075 402.59 < 2.2e-16 *** Residuals 27 0.1346 0.0050 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(dsDalllm) Call: lm(formula = outcome ~ model - 1, data = datasetD) Residuals: Min 1Q Median 3Q Max -0.143218 -0.033901 -0.003868 0.068896 0.145228 Coefficients: Estimate Std. Error t value Pr(>|t|) modelANN 0.27837 0.02445 11.383 8.22e-12 *** modelGLM 0.15218 0.02445 6.223 1.18e-06 *** modelSVM 0.21519 0.02445 8.800 2.05e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.07733 on 27 degrees of freedom Multiple R-Squared: 0.901, Adjusted R-squared: 0.89 F-statistic: 81.92 on 3 and 27 DF, p-value: 1.120e-13 > anova(dsDalllm) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 3 1.46961 0.48987 81.916 1.120e-13 *** Residuals 27 0.16146 0.00598 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(dsEalllm) Call: lm(formula = outcome ~ model - 1, data = datasetE) Residuals: Min 1Q Median 3Q Max -0.09647 -0.04015 -0.01348 0.04062 0.11922 Coefficients: Estimate Std. Error t value Pr(>|t|) modelANN 0.27897 0.01828 15.26 8.44e-15 *** modelGLM 0.20079 0.01828 10.99 1.82e-11 *** modelSVM 0.27116 0.01828 14.84 1.68e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.0578 on 27 degrees of freedom Multiple R-Squared: 0.9551, Adjusted R-squared: 0.9501 F-statistic: 191.3 on 3 and 27 DF, p-value: < 2.2e-16 > anova(dsEalllm) Analysis of Variance Table Response: outcome Df Sum Sq Mean Sq F value Pr(>F) model 3 1.91670 0.63890 191.26 < 2.2e-16 *** Residuals 27 0.09019 0.00334 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > plot(outcome ~ model*set,data = datasubset) > > boxplot(AA, AB, AC, AD, AE, AF, AG, BA, BB, BC, BD, BE, BF, BG, CA, CB, CC, CD, CE, CF, CG, ylim=c(0,1)) > boxplot(AA, AB, AC, AD, AE, AF, AG, AH, NULL, BA, BB, BC, BD, BE, BF, BG, BH, NULL, CA, CB, + CC, CD, CE, CF, CG, CH, NULL, AA, BA, CA, NULL, AB, BB, CB, NULL, AC, BC, CC, NULL, AD, BD, + CD, NULL, AE, BE, CE, NULL, AF, BF, CF, NULL, AG, BG, CG, AH, BH, CH, ylim=c(0,1)) >