I'm new to package mice in R. But I'm trying to impute 5 datasets from popmis and then fit an lmer() model with() each and finally pool() across them.
I think the pool() function in mice() doesn't work with the lmer() call from lme4 package, right?
If that is the case, is there a way to write a custom-made function that acts like pool() for my case below?
library(mice)
library(lme4)
imp <- mice(popmis, m = 5) # `popmis` is a dataset from `mice`
fit <- with(imp, lme4::lmer(popular ~ sex + (1|school))) # works fine.
pool(fit) # BUT this one fails, should I loop here?
I have the solution for you. It is as simple as install.packages("broom.mixed") and then library(broom.mixed).broom.mixed package is providing the proper glance method
# install.packages("broom.mixed")
library(mice)
library(lme4)
library(broom.mixed)
imp <- mice(popmis, m = 5) # `popmis` is a dataset from `mice`
fit <- with(data = imp, exp = lme4::lmer(popular ~ sex + (1|school)))
pool(fit)
Results:
> pool(fit)
Class: mipo m = 5
term m estimate ubar b t dfcom df riv lambda fmi
1 (Intercept) 5 4.9122016 0.007589694 0.0003823641 0.008048531 1996 743.8691 0.06045526 0.05700878 0.05953397
2 sex 5 0.8378947 0.001187606 0.0002937859 0.001540149 1996 72.7305 0.29685175 0.22890184 0.24926611