Confidence intervals for effects in glmer()
It depends on what approximations you're willing to accept. Crudely doing +/- 2 standard deviations, or (DANGER DANGER) using the 'known' df to compute t-scores (the "gm1" example given in ?glmer has a fairly straightforward structure, with 15 groups): library(lme4) example(glmer) s <- summary(gm1)@coefs fac <- 2 s[,"Estimate"]+fac*outer(c(-1,1),s[,"Std. Error"]) fac <- qt(0.975,df=14) s[,"Estimate"]+fac*outer(c(-1,1),s[,"Std. Error"]) Does anyone know the current status of mcmcsamp, either for LMMs or for GLMMs ... ? Ben Bolker
Robert A. LaBudde wrote:
This may be blindingly obvious to the casual observer, but I'm
chagrined to admit I'm stumped.
I'm fitting a simple mixed effect logistic model using 'lme4':
require('lme4')
fit4<- glmer(x ~ 1 + 1|lab, data=eg, nAGQ=5, family='binomial')
summary(fit4)
ranef(fit4)
I would like 95% confidence intervals on 'lab' and the residuals effects.
Using lme() in 'nlme', I had the function intervals() available. Now I don't.
Any hints to de-perplex a novice?
Thanks.
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Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: ral at lcfltd.com
Least Cost Formulations, Ltd. URL: http://lcfltd.com/
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