I can run the rlmer model with the object that results from mice, but when I try to pool the results a get the message Error: No tidy method for objects of class rlmerMod. Is there an alternative?
Below there is a reproducible example of my data and models:
set.seed(1)
library(data.table)
library(robustlmm)
library(mice)
library(miceadds)
dt <- data.table(id = rep(1:10, each=3),
group = rep(1:2, each=15),
time = rep(1:3, 10),
sex = rep(sample(c("F","M"),10,replace=T), each=3),
x = rnorm(30),
y = rnorm(30))
setDT(dt)[id %in% sample(1:10,4) & time == 2, `:=` (x = NA, y = NA)][
id %in% sample(1:10,4) & time == 3, `:=` (x = NA, y = NA)]
# Multiple imputation -------------------------------------------------------------------
pm <- make.predictorMatrix(dt)
pm[,c('x','y')] <- 0
pm[c('x','y'), 'id'] <- -2
imp <- mice(dt, pred = pm, meth = "2l.pmm", seed = 1, m = 2, print = FALSE, maxit = 20)
# Modelling -----------------------------------------------------------------------------
m <- with(imp, rlmer(y ~ 1 + time * group + sex + (1 | id), REML=F))
pool.fit <- pool(m)
> pool.fit <- pool(m)
Error: No tidy method for objects of class rlmerMod
In addition: Warning message:
In get.dfcom(object, dfcom) : Infinite sample size assumed. # I don't get this warning using my real data
Thank you!
EDIT:
As commented by @BenBolker, library(broom.mixed) gets pool.fit to run without errors. Hovever, summary(pool.fit,conf.int = TRUE) returns the estimates, but NaN for degrees of freedom, p values and confidence intervals.
library(broom.mixed)
pool.fit <- pool(m)
summary(pool.fit,conf.int = TRUE)
term estimate std.error statistic df p.value 2.5 % 97.5 %
1 (Intercept) -1.31638288 1.2221584 -1.07709683 NaN NaN NaN NaN
2 time 0.02819273 0.4734632 0.05954578 NaN NaN NaN NaN
3 group 1.49581955 0.8776475 1.70435124 NaN NaN NaN NaN
4 sexM -0.61383469 0.7137998 -0.85995356 NaN NaN NaN NaN
5 time:group -0.25690287 0.3005254 -0.85484573 NaN NaN NaN NaN
I don't know if another parameter is needed (eg., for defining the df method).
For now, I tried tbl_regression(m) but it didn't work either:
> tbl_regression(m)
pool_and_tidy_mice(): Tidying mice model with
`mice::pool(x) %>% mice::tidy(exponentiate = FALSE, conf.int = TRUE, conf.level = 0.95)`
Error in match.call() : ... used in a situation where it does not exist # how to correct this?
In addition: Warning message:
In get.dfcom(object, dfcom) : Infinite sample size assumed. # again, this warning don't occur with my original data
Any tip?
Just load the broom.mixed package, which has tidiers for rlmerMod objects. (The development version of broom.mixed has a get_methods() function:
remotes::install_github("bbolker/broom.mixed")
library(broom.mixed)
print(get_methods(), n = Inf)
# A tibble: 22 × 4
class tidy glance augment
<chr> <lgl> <lgl> <lgl>
1 allFit TRUE TRUE FALSE
2 brmsfit TRUE TRUE TRUE
3 gamlss TRUE TRUE FALSE
4 gamm4 TRUE TRUE TRUE
5 glmmadmb TRUE TRUE TRUE
6 glmmTMB TRUE TRUE TRUE
7 gls TRUE TRUE TRUE
8 lme TRUE TRUE TRUE
9 lmList4 TRUE FALSE FALSE
10 mcmc TRUE FALSE FALSE
11 mcmc.list TRUE FALSE FALSE
12 MCMCglmm TRUE FALSE FALSE
13 merMod TRUE TRUE TRUE
14 MixMod TRUE FALSE FALSE
15 ranef.mer FALSE FALSE TRUE
16 rjags TRUE FALSE FALSE
17 rlmerMod TRUE FALSE FALSE
18 stanfit TRUE FALSE FALSE
19 stanreg TRUE TRUE FALSE
20 TMB TRUE FALSE FALSE
21 varComb TRUE FALSE FALSE
22 varFunc TRUE FALSE FALSE