## load package
library("margins")
library(lme4)

## code goes here
d=data.frame(cat=c(1,0,0,1), bird=c(1,0,2,1), room=c(1,1,0,0))
m=glmer(cat~bird + (1 | room), data=d, family=binomial(link="logit"))
margins(m, vce="bootstrap", iterations=999)

## session info for your system
R version 3.5.1 (2018-07-02)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux

Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8
 [8] LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] lme4_1.1-18    Matrix_1.2-14  margins_0.3.23

loaded via a namespace (and not attached):
 [1] minqa_1.2.4       MASS_7.3-50       .py_0.1.3         compiler_3.5.1    tools_3.5.1       Rcpp_0.12.18      splines_3.5.1     nlme_3.1-137      grid_3.5.1        data.table_1.11.4
[11] nloptr_1.0.4      lattice_0.20-35   prediction_0.3.6

Error in `*tmp*`[["call"]] : this S4 class is not subsettable

> traceback()
12: bootfun()
11: FUN(X[[i]], ...)
10: lapply(X = X, FUN = FUN, ...)
9: sapply(integer(n), eval.parent(substitute(function(...) expr)),
       simplify = simplify)
8: replicate(iterations, bootfun())
7: t(replicate(iterations, bootfun()))
6: is.data.frame(x)
5: var(t(replicate(iterations, bootfun())))
4: get_effect_variances(data = data, model = model, variables = variables,
       type = type, vcov = vcov, vce = vce, iterations = iterations,
       weights = weights, eps = eps, varslist = varslist, ...)
3: build_margins(model = model, data = data_list[[i]], variables = variables,
       type = type, vcov = vcov, vce = vce, iterations = iterations,
       unit_ses = unit_ses, eps = eps, varslist = varslist, ...)
2: margins.merMod(m, vce = "bootstrap", iterations = 999)
1: margins(m, vce = "bootstrap", iterations = 999)