setwd("G:/1_Chapter2_STUDY1/DATA_STUDY1/1_Analysis paper2018") library(lme4); library(lmerTest); library(stats); library(ggplot2); library(readr); library(readxl) library(Rmisc); library(pastecs); library(psych); library(multcomp) install.packages("gmodels") library(gmodels); library(MASS); library(Hmisc) #### Perso traits #### Perso <- read_excel("G:/1_STUDY1/DATA_STUDY1/1_Analysis paper2018/DATA_PersoTrait_Expressivity_2019.xlsx") View(Perso) Personality <- glm(Guilt2 ~ Extraversion + Agreebleness + Conscientiousness + EmotionalStability + Openness + Machiavelism + Psychopathy + Narcissism, dat = Perso) summary(Personality) t.test(Tot_IndexExp~CultureGp, dat=Perso) ddply(Perso, ~CultureGp, summarise, mean=mean(Tot_IndexExp), sd=sd(Tot_IndexExp)) t.test(Tot_IndexExp~Condition, dat=Perso) ddply(Perso, ~Condition, summarise, mean=mean(Tot_IndexExp), sd=sd(Tot_IndexExp)) Affect <- read_excel("G:/1_STUDY1/DATA_STUDY1/1_Analysis paper2018/DATA_PersoTrait_Expressivity_2019.xlsx", sheet = "Guilt") View(Affect) describe(Affect) t.test(Affect$`Negative1`,Affect$`Negative2`, paired = TRUE) t.test(Affect$`Positive1`,Affect$`Positive2`, paired = TRUE) t.test(Affect$`Guilt1`,Affect$`Guilt2`, paired = TRUE) t.test(Affect$`Shame1`,Affect$`Shame2`, paired = TRUE) t.test(Affect$`Distressed1`,Affect$`Distressed2`, paired = TRUE) t.test(Affect$`Proud1`,Affect$`Proud2`, paired = TRUE) t.test(Affect$`Shame2`, Affect$`Guilt2`, paired = TRUE) Affect <- read_excel("G:/1_STUDY1/DATA_STUDY1/1_Analysis paper2018/DATA_PersoTrait_Expressivity_2019.xlsx", sheet = "Control") View(Affect) describe(Affect) t.test(Affect$`Negative1`,Affect$`Negative2`, paired = TRUE) t.test(Affect$`Positive1`,Affect$`Positive2`, paired = TRUE) t.test(Affect$`Guilt1`,Affect$`Guilt2`, paired = TRUE) t.test(Affect$`Shame1`,Affect$`Shame2`, paired = TRUE) t.test(Affect$`Distressed1`,Affect$`Distressed2`, paired = TRUE) t.test(Affect$`Proud1`,Affect$`Proud2`, paired = TRUE) t.test(Affect$`Shame2`, Affect$`Guilt2`, paired = TRUE) #### DO NOT USE ANYMORE #### ### AUs selection process -Factors-Guilt ### ##GLMM guilt - all AUs (instead of correlations?) AUfile <- read_excel("G:/1_STUDY1/DATA_STUDY1/1_Analysis paper2018/FACS file formatted.xlsx", sheet = "Sheet1") View (AUfile) summary(glm(Guilt2 ~ AU1 + AU2 + AU4 + AU5 + AU6 + AU7 + AU9 + AU10_11 + AU12 + AU14 + AU15 + AU16 + AU17 + AU18 + +AU20 + AU22 + AU23_24 + AU28 + AU37 + AU51 + AU61 + AU52 + AU62 + AU53 + AU63 + AU54 + AU64 + AU55 + AU56 + AU57 + AU58 + AU59 + NeckTouch + FaceTouch, dat= AUfile)) res <- rcorr(as.matrix(AUfile)) res2 <- corr.p(res$r, 64, adjust = "bonferroni") #PCA <- read_csv("G:/1_STUDY1/DATA_STUDY1/1_Analysis paper2018/FactorsPCA1.csv") FactorPCA <- read_excel("G:/1_STUDY1/DATA_STUDY1/1_Analysis paper2018/Factors PCA.xlsx") View(FactorPCA) SelectionGuilt<- glm(Guilt2 ~ PCA1 + PCA2 + PCA3 + PCA4 + NeckTouch, dat=FactorPCA) summary(SelectionGuilt) #REMOVE FACTOR PCA2, PCA3 AND PCA4 - only keep factor POSITIVELY correlating with self-reported guilt ### GLM on aggreagated file ### Guilt<- read_excel("G:/1_STUDY1/DATA_STUDY1/1_Analysis paper2018/FACS_Aggregated_both conditions.xlsx", sheet = "Guilt") summary(glm(Shame2 ~ AU10_11 + NeckTouch, dat=Guilt)) summary (glm(Pride2 ~ AU10_11 + NeckTouch, dat=Guilt)) Control <- read_excel("G:/1_STUDY1/DATA_STUDY1/1_Analysis paper2018/FACS_Aggregated_both conditions.xlsx", sheet = "Control") summary(glm(Shame2 ~ AU62, dat=Control)) summary (glm(Pride2 ~ AU62, dat=Control)) # PBM with long file: some participants have 0 on both factors + too many 0s PCA <- read_excel("G:/1_STUDY1/DATA_STUDY1/1_Analysis paper2018/FactorsFinalaggr.xlsx") View(PCA) UniversalGuiltModel1<- glm(F1 ~ Guilt2, dat=PCA) summary(UniversalGuiltModel1) UniversalGuiltModel2<- glm(F2 ~ Guilt2, dat=PCA) summary(UniversalGuiltModel2) UniversalShameModel1<- glm(F1 ~ Shame2, dat=PCA) summary(UniversalShameModel1) UniversalShameModel2<- glm(F2 ~ Shame2, dat=PCA) summary(UniversalShameModel2) UniversalPrideModel1<- glm(F1 ~ Pride2, dat=PCA) summary(UniversalPrideModel1) UniversalPrideModel2<- glm(F2 ~ Pride2, dat=PCA) summary(UniversalPrideModel2) PCA_Eu <- read_excel("G:/1_STUDY1/DATA_STUDY1/1_Analysis paper2018/FactorsFinalEuropeaggr.xlsx") View(PCA_Eu) EuropeGuiltModel1<- glm(F1 ~ Guilt2, dat=PCA_Eu) summary(EuropeGuiltModel1) EuropeGuiltModel2<- glm(F2 ~ Guilt2, dat=PCA_Eu) summary(EuropeGuiltModel2) PCA_As <- read_excel("G:/1_STUDY1/DATA_STUDY1/1_Analysis paper2018/FactorsFinalAsianaggr.xlsx") View(PCA_As) AsianGuiltModel1<- glm(F1_EA ~ Guilt2, dat=PCA_As) summary(AsianGuiltModel1) AsianGuiltModel2<- glm(F2_EA ~ Guilt2, dat=PCA_As) summary(AsianGuiltModel2) #### #### ### GLM on aggreagated file ### Guilt<- read_excel("G:/1_Chapter2_STUDY1/DATA_STUDY1/1_Analysis paper2018/FACS_Aggregated_both conditions.xlsx", sheet = "Guilt") summary(glm(Shame2 ~ AU4 + AU20 + AU52 + AU62 + NeckTouch, dat=Guilt)) summary (glm(Pride2 ~ AU4 + AU20 + AU52 + AU62 + NeckTouch, dat=Guilt))