Hi all, I am fitting a linear mixed model with lme4 in R. The model has a single factor (des_days) with 4 levels (-1,1,14,48), and I am using random intercept and slopes.
Fixed effects: data ~ des_days Value Std.Error DF t-value p-value (Intercept) 0.8274313 0.007937938 962 104.23757 0.0000 des_days1 -0.0026322 0.007443294 962 -0.35363 0.7237 des_days14 -0.0011319 0.006635512 962 -0.17058 0.8646 des_days48 0.0112579 0.005452614 962 2.06469 0.0392 I can clearly use the previous results to compare the estimations of each "des_day" to the intercept, using the provided t-statistics. Alternatively, I could use post-hoc tests (z-statistics): > ph_conditional <- c("des_days1 = 0", "des_days14 = 0", "des_days48 = 0"); > lev.ph <- glht(lev.lm, linfct = ph_conditional); > summary(lev.ph) Simultaneous Tests for General Linear Hypotheses Fit: lme.formula(fixed = data ~ des_days, data = data_red_trf, random = ~des_days | ratID, method = "ML", na.action = na.omit, control = lCtr) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) des_days1 == 0 -0.002632 0.007428 -0.354 0.971 des_days14 == 0 -0.001132 0.006622 -0.171 0.996 des_days48 == 0 0.011258 0.005441 2.069 0.101 (Adjusted p values reported -- single-step method) The p-values of the coefficient estimates and those of the post-hoc tests differ because the latter are adjusted with Bonferroni correction. I wonder whether there is any form of correction in the coefficient estimated of the LMM, and which p-values are more appropriate to use. Thanks Cristiano [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.