search for: dt_pur

...p;rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA &url=http%3A%2F%2Fwww.ualberta.ca%2F~baayen%2Fpublications%2FbaayenDavidsonB ates.pdf&ei=FhqTUoXuJKKV7Abds4GYBA&usg=AFQjCNFst7GT7mBX7w9lXItJTtELJSKWJg&si g2=KGA5MHxOvEGwDxf-Gcqi6g&bvm> R.H. et al 2008) Here, dT_purs is the response variable, T and Z are the fixed effects, and subject is the random effect. Random and fixed effects are crossed.: mod0 <- lmer(dT_purs ~ T + Z + (1|subject), data = x) mod1 <- lmer(dT_purs ~ T + Z + (1 +tempo| subject), data = x) mod2 <- lmer(dT_purs ~ T + Z + (1 +tempo...

Dear Thierry, thank you for the quick reply. I have only one question about the approach you proposed. As you suggested, imagine that the model we end up after the model selection procedure is: mod2.1 <- lmer(dT_purs ~ T + Z + (1 +T+Z| subject), data =x, REML= FALSE) According to the common procedures specified in many manuals and recent papers, if I want to compute the p_values relative to each term, I will perform a likelihood test, in which the deviance of the (-2LL) of a model containing the specific term...