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

[marKo]

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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