[R-sig-ME] p-correction for effects in LMM
v|ctor|@@w||||t@ @end|ng |rom gm@||@com
Thu Dec 2 15:02:50 CET 2021
That is great thank you for the quick reply Marko :)
On Wed, Dec 1, 2021 at 7:48 AM marKo <mtoncic using ffri.uniri.hr> wrote:
> Not sure what to say in this regard, as those methods will produce very
> similar results. If I recall it correctly, Douglas Bates suggests doing
> a profile CI.
> I usually do a bootstrap and do not think much about it (sorry to say
> that, actually).
> As for the number of cores (CORES in the mentioned code), they depends
> on the processor you have. To establish the max number, in Windows start
> the task manager and see how many threads you have. In Linux, You can
> use some system monitor to check that.
> Hope it helps,
> On 30. 11. 2021. 16:40, Victoria Pattison-Willits wrote:
> > 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
> > lmer with a nested random effect crossed with a second random effect and
> > 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
> > 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
> > 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 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
> >>> 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
> >>> 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.
> >> --
> >> 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
> >> _______________________________________________
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> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
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