Cristiano,
First, I want to make clear that you understand that (1) the intercept is
really the estimate of the mean when des_days = -1, and that (2) the
multiplicity correction used in the test is based on the multivariate t
distribution (not Bonferroni). As such, it is less conservative than
Bonferroni, and is "exact" in that if the underlying assumptions are
exactly true, the probability of at least one type-I error is controlled at
the desired level. (That said, the P values are actually computed using a
simulation method, so they will vary a bit if you call glht again).
If you really only care about comparisons with the -1 level, I think what
you have is a good solution. Some people want to use weaker control of the
error rate. In that case, you can use
summary(glht(...), test = adjusted("desired choice"))
(see the help file for summary.glht), which gives you other choices
besides the single-step method that it defaults to. You could in fact
specify adjusted("none") to get no adjustments, or adjusted("bonferroni"),
adjusted("fdr"), etc. if you want to use one of the standard methods in
stats::p.adjust.methods.
Often, people want to compare *all* pairs of treatments; and if that's the
case, you can specify that using a call to mcp() in the linfct argument of
glht.
Russ
Russell V. Lenth - Professor Emeritus
Department of Statistics and Actuarial Science
The University of Iowa - Iowa City, IA 52242 USA
Voice (319)335-0712 (Dept. office) - FAX (319)335-3017
On 18-03-22 01:28 PM, Cristiano Alessandro wrote:
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