log scale, same normalization of colors across plots, plot showing all variables at once.
two axes, color, and shape to indicate subject, dataset, session, parallel coordinates plot. for each ROI/ROI pair, plot each of the different effects.
gender as a fixed effect. session not necessary to put in at all, or as a fixed effect if you expected 1 session to be systematically different. this is probably why session has 0 random effect component. people w 2 sessions, the variability around sessions is about the same, that's why we get 0. the variability across sessions is already captured in the subject label.
clean up model, clean up markdown, clean up simulations.
log scale, same normalization of colors across plots, plot showing all variables at once.
two axes, color, and shape to indicate subject, dataset, session, parallel coordinates plot. for each ROI/ROI pair, plot each of the different effects.
gender as a fixed effect. session not necessary to put in at all, or as a fixed effect if you expected 1 session to be systematically different. this is probably why session has 0 random effect component. people w 2 sessions, the variability around sessions is about the same, that's why we get 0. the variability across sessions is already captured in the subject label.
clean up model, clean up markdown, clean up simulations.