VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data
Figure 10
This figure exemplifies a classification analysis, which is used to infer on the link between a continuous variable X and a binary data y. The analysis is conducted on data simulated under either a null model (H0: no link) or a sigmoid mapping (H1). Upper left: the classification accuracy, in terms of the Monte-Carlo average probability of correct prediction under both types of data (left: H1, right: H0), for the training dataset. The green dots show the expected classification accuracy, using the true values of each model's set of parameters. The dotted red line depicts chance level. Upper right: same format, test dataset (no model fitting). Lower left: same format, for the log Bayes factor , given the training dataset. Lower right: same format, given the full (train+test) dataset.