[R-sig-ME] p-correction for effects in LMM
v|ctor|@@w||||t@ @end|ng |rom gm@||@com
Tue Nov 30 16:40:32 CET 2021
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! :)
On Fri, Nov 26, 2021 at 2:30 PM marKo <mtoncic using ffri.uniri.hr> 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
> change No.of.CORES with the desired number of cores (depends of your
> Hope it helps.
> Marko Tončić, PhD
> Assistant professor
> University of Rijeka
> Faculty of Humanities and Social Sciences
> Department of Psychology
> Sveucilisna avenija 4, 51000 Rijeka, CROATIA
> e-mail: mtoncic using ffri.uniri.hr
> R-sig-mixed-models using r-project.org mailing list
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