R: Bootstrapped binary mixed-model logistic regression using bootMer() of the new lme4 package

Question

I want to use the new bootMer() feature of the new lme4 package (the developer version currently). I am new to R and don\'t know which function should I write for its FUN argume

Answer 1

There are basically two (simple) confusions here.

• The first is between coef() (which returns a list of matrices) and fixef() (which returns a vector of the fixed-effect coefficients): I assume that fixef() is what you wanted, although you might want something like c(fixef(mixed),unlist(VarCorr(mixed))).
• the second is that FUN should take a fitted model object as input ...

For example:

library(lme4)
library(boot)

mixed <- glmer(incidence/size ~ period + (1|herd),
               weights=size, data=cbpp, family=binomial)

FUN <- function(fit) {
    return(fixef(fit))
}

result <- bootMer(mixed, FUN, nsim = 3)

result

## Call:
## bootMer(x = mixed, FUN = FUN, nsim = 3)
## Bootstrap Statistics :
##      original      bias    std. error
## t1* -1.398343 -0.20084060  0.09157886
## t2* -0.991925  0.02597136  0.18432336
## t3* -1.128216 -0.03456143  0.05967291
## t4* -1.579745 -0.08249495  0.38272580
##