p-correction for effects in LMM

Dear Marko,
Yes, I need effect size for F tests or p-adjustment for it. Thus, is 
there any procedure to obtain effect sizes or if I use p-adjustments I 
am not sure whether I need to involve random effect in calculation or 
not?
Thank you.

Regards,
Bojana

On 04-Dec-21 23:38, marKo wrote:

I must admit that I do not understand what is that you are asking. 
Those CIs are for the parameters of your model (a 3x4 model + random 
effects: subject + residuals).
The referent group here are cond2 and rep1.
Maybe the problem that you have is that you would like to have an F 
statistic for the main effects and for the interaction. I do not know.

Marko


On 04. 12. 2021. 11:26, Bojana Dinic wrote:

Dear Marko,

Thank you. I have question, these are CIs for which statistic (I 
have 2 factors, cond and rep, and their interaction)?
????????????????????????? 2.5 %??? 97.5 %
.sig01??????? 8.6062568 12.035500
.sigma?????? 12.8489647 14.841375
(Intercept)? -2.1807253? 9.047992
cond1??????? -4.1126524 11.296070
cond3??????? -5.8346317? 7.649526
rep2???????? -8.0280001? 5.220439
rep3???????? -4.0846168? 9.194793
rep4????????? 6.1875602 18.878770
cond1:rep2? -11.2367698? 8.031134
cond3:rep2?? -7.9112913? 7.491230
cond1:rep3?? -8.2536791 10.129607
cond3:rep3?? -5.9766989 10.071404
cond1:rep4?? -0.9371539 17.551132
cond3:rep4?? -4.2738610 11.020311

Kind regars,
Bojana

On 26-Nov-21 20:29, marKo wrote:

On 26. 11. 2021. 08:41, Bojana Dinic wrote:

Dear colleagues,

???? I use linear mixed models with 1 random effect (subject), 2 
fixed
???? factors (one? is between factor and another is repeated) and 
one covariate, and
???? explore all main effects, 2-way interactions and one 3-way 
interaction.
???? Regarding of used software, somewhere I get effect of intercept,
???? somewhere not. Reviewer asks to use p-adjustment for these
???? effects. My dilemma is should I apply p-correction for 7 
tests or 8 (including
???? random intercept for subjects)?

???? The output do not contain F for random effect, but only 
variance.
???? Also, the output do not contain effect size. CIs are 
available only for
???? betas as product of specific level of both fixed effects and 
covariate, but
???? since I have 3 levels for between and 4 for repeated effects, 
the
???? output is not helpful + there is no possibility to change 
reference group.
???? Thus, I'm stuck with p-adjustment.

??? Any help is welcomed.
???? Thank you.

As I understand, p-values are somewhat unreliable (In LMM). As a 
sensible alternative maybe you could compute bootstrap CI and use 
that to infer about significance of specific effects (if i have 
understood your problem correctly).
I you use lme4 or nlme, this should not be a problem.

You ca use (for model? m)

confint(m, level=0.95, method="boot", nsim=No.of.SIMULATIONS)

even use some multi-core processing to speed thing up

confint(m, level=0.95, method="boot", parallel = "multicore", ncpus 
= No.of.CORES, nsim=No.of.SIMULATIONS)

change No.of.SIMULATIONS with the desired number of repetitions 
(1000 or so)
change No.of.CORES with the desired number of cores (depends of 
your machine).

Hope it helps.