search for: p_valuet

...results. Each comparison returns a significance associated with the estimated coefficient of the term. Thus in this case: mod2.2 <- lmer(dT_purs ~ Z + (1 +T+Z|soggetto) , data = x, REML = FALSE) mod2.3 <- lmer(dT_purs ~ T + (1 +T+Z|soggetto) , data = x, REML = FALSE) anova(mod2.1, mod2.2) p_valueT = 3.203e-05 anova(mod2.1, mod2.3) p_valueZ = 0.001793 What about the p_value relative to the (1+T+Z|subject)? One option is to compute: mod2.4 <- lm(dT_purs ~ T + (1 +T+Z|soggetto) , data = x) and then execute the loklikelihood test as follows: L0 <-logLik(mod2.4) L1 <-logLik(mod2.1) LR...

Hi All, I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA