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p-correction for effects in LMM

3 messages · Bojana Dinic, marKo, Victoria Pattison-Willits

Original
Bojana Dinic
# 
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.
marKo
#
On 26. 11. 2021. 08:41, Bojana Dinic wrote:
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.
3 days later
Victoria Pattison-Willits
# 
Hi there
Thank you to the OP for sharing this question and I am following this
thread as I was wondering which CIs were the best to go with for mixed
models - I have been calculating three different types (Wald, Boot and
Profile) and was not really sure for mixed models (in my case v similar an
lmer with a nested random effect crossed with a second random effect and 8
fixed effects (no interactive terms)). I have been on a massive learning
curve and so still a little hazy how the t approaches differ in their calc
of  the CIs - I have been reporting the bootstrap CIs in my project
although there was only a very small difference when plotted for all 3
across all my fixed effects. Just want to check in light of this question
this is the correct approach!

One also quick q related to OQ - how do you determine the number of CORES
if I wanted to include that code - does it depend on processing speeds etc?

Cheers and this is my first question and I still am a relative novice so
thanks in advance for patience with probably very simple questions! :)

Vicki PW
On Fri, Nov 26, 2021 at 2:30 PM marKo <mtoncic at ffri.uniri.hr> wrote: