[R-sig-ME] p-correction for effects in LMM

[Bojana Dinic]

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.
>
>