p-correction for effects in LMM
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.
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