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

Bojana Dinic boj@n@@d|n|c @end|ng |rom gm@||@com
Mon Dec 6 23:04:16 CET 2021


Dear Marko,
Yes, I need effect size for F tests or p-adjustment for it. Thus, is 
there any procedure to obtain effect sizes or if I use p-adjustments I 
am not sure whether I need to involve random effect in calculation or not?
Thank you.

Regards,
Bojana

On 04-Dec-21 23:38, marKo wrote:
> 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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