[R-sig-ME] p-correction for effects in LMM
marko
mtonc|c @end|ng |rom ||r|@un|r|@hr
Tue Dec 7 23:32:14 CET 2021
I think that the only viable option (at least that i know of; please
someone from the group to back me up on this) is to compare
competing/nested models (the ones with and without some specific
parameters; e.g. the model without and with the interaction parameters)
via LR test ("anova(m1, m2)" in R).
As an estimate of effect size, you can compute omega^2 (even though it
is just a pseudo R^2 measure; a mere squared correlation between
predicted and actual results) for those competing/nested models. See
more in;
Xu, R. (2003). Measuring explained variation in linear mixed effects
models. Statistics in Medicine, 22(22), 3527–3541.
https://doi.org/10.1002/sim.1572
I think that some of that is implemented in the package "sjstats" if
this might be of any help.
Cheers,
Marko
On 12/6/21 11:04 PM, Bojana Dinic wrote:
> 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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