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List: r-sig-mixed-models
Subject: Re: [R-sig-ME] Running corrections for multiple comparisons in glmer
From: Ben Bolker <bbolker () gmail ! com>
Date: 2019-10-31 14:58:14
Message-ID: e2a08ceb-0079-1fff-1220-60cf704830ba () gmail ! com
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you could "just" use p.adjust(), something like this:
library(lme4)
gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial)
cc <- coef(summary(gm1))
cc <- cbind(cc,adjust.p=p.adjust(cc[,"Pr(>|z|)"],"holm"))
The general machinery in the multcomp package (especially the glht
function) should work.
This looks useful:
https://thebiobucket.blogspot.com/2011/06/glmm-with-custom-multiple-comparisons.html
The bottom line is that most standard multiple-comparisons or
pairwise-comparisons machinery should "just work" with glmer fits.
(There are some open questions about what you're doing: it's a bit
unusual for people to apply multiple comparisons corrections on a set of
"only" 6 parameters specified a priori: Tukey adjustments to post hoc
pairwise comparisons are much more common ...)
On 2019-10-31 10:36 a.m., Francesco Romano wrote:
> Dear all,
>
> A reviewer has asked me to apply a correction to multiple comparisons
> conducted for a logistic mixed effect regression with binary outcome. The
> model is:
>
> glmer(outcome ~ factor1 * factor2 + (1|RE1) + (1|RE2), family =binomial,
> data = data)
>
> where factor 1 has two levels and factor 2 has three. Could you advise on
> how to run this and how to report the adjusted p-values in the same table?
> At the moment, my table has the following 6 headings:
>
> Reference level
>
> Contrasts
>
> Estimate
>
> SE
>
> Wald *z*
>
> *p*
>
>
> Many thanks in advance,
>
> Frank
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
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