[R-sig-ME] adjusted values
Cristiano Alessandro
cri.alessandro at gmail.com
Thu Mar 22 18:28:30 CET 2018
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
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