p-correction for effects in LMM
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:
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. -- Marko Ton?i?, PhD Assistant professor University of Rijeka Faculty of Humanities and Social Sciences Department of Psychology Sveucilisna avenija 4, 51000 Rijeka, CROATIA e-mail: mtoncic at ffri.uniri.hr
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