[R-sig-ME] p-correction for effects in LMM
boj@n@@d|n|c @end|ng |rom gm@||@com
Sat Dec 4 11:26:17 CET 2021
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
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
>> 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
> Hope it helps.
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