Automatically compare nested models from mice's glm.mids - r

Question

I have a multiply-imputed model from R's mice package in which there are lots of factor variables. For example:
library(mice)
library(Hmisc)
# turn all the variables into factors
fake = nhanes
fake$age = as.factor(nhanes$age)
fake$bmi = cut2(nhanes$bmi, g=3)
fake$chl = cut2(nhanes$chl, g=3)
head(fake)
age bmi hyp chl
1 1 <NA> NA <NA>
2 2 [20.4,25.5) 1 [187,206)
3 1 <NA> 1 [187,206)
4 3 <NA> NA <NA>
5 1 [20.4,25.5) 1 [113,187)
6 3 <NA> NA [113,187)
imput = mice(nhanes)
# big model
fit1 = glm.mids((hyp==2) ~ age + bmi + chl, data=imput, family = binomial)
I want to test the significance of each entire factor variable in the model (not the indicator variables for each level) by testing the full model against each possible nested model that drops one variable at a time. Manually, I can do:
# small model (no chl)
fit2 = glm.mids((hyp==2) ~ age + bmi, data=imput, family = binomial)
# extract p-value from pool.compare
pool.compare(fit1, fit2)$pvalue
How can I do this automatically for all the factor variables in my model? The very helpful function drop1 was suggested to me for a previous question -- now I want to do something exactly like that except for the mice case.
Possibly helpful note: An annoying feature of pool.compare is that it appears to want the "extra" variables in the larger model to be placed after the ones that are shared with the smaller model.

Answer 1

You can use a loop to iterate through the different combinations of predictors, after arranging them in the order required for pool.compare.
So using your fake data from above - tweaked the number of categories
library(mice)
library(Hmisc)
# turn all the variables into factors
# turn all the variables into factors
fake <- nhanes
fake$age <- as.factor(nhanes$age)
fake$bmi <- cut2(nhanes$bmi, g=2)
fake$chl <- cut2(nhanes$chl, g=2)
# Impute
imput <- mice(fake, seed=1)
# Create models
# - reduced models with one variable removed
# - full models with extra variables at end of expression
vars <- c("age", "bmi", "chl")
red <- combn(vars, length(vars)-1 , simplify=FALSE)
diffs <- lapply(red, function(i) setdiff(vars, i) )
(full <- lapply(1:length(red), function(i)
paste(c(red[[i]], diffs[[i]]), collapse=" + ")))
#[[1]]
#[1] "age + bmi + chl"
#[[2]]
#[1] "age + chl + bmi"
#[[3]]
#[1] "bmi + chl + age"
(red <- combn(vars, length(vars)-1 , FUN=paste, collapse=" + "))
#[1] "age + bmi" "age + chl" "bmi + chl"
The models are now in the correct order to pass to the glm call. I've also replaced glm.mids method as it has been replaced by with.mids - see ?glm.mids
out <- vector("list", length(red))
for( i in 1:length(red)) {
redMod <- with(imput,
glm(formula(paste("(hyp==2) ~ ", red[[i]])), family = binomial))
fullMod <- with(imput,
glm(formula(paste("(hyp==2) ~ ", full[[i]])), family = binomial))
out[[i]] <- list(predictors = diffs[[i]],
pval = c(pool.compare(fullMod, redMod)$pvalue))
}
do.call(rbind.data.frame, out)
# predictors pval
#2 chl 0.9976629
#21 bmi 0.9985028
#3 age 0.9815831
# Check manually by leaving out chl
mod1 <- with(imput, glm((hyp==2) ~ age + bmi + chl , family = binomial))
mod2 <- with(imput, glm((hyp==2) ~ age + bmi , family = binomial))
pool.compare(mod1, mod2)$pvalue
# [,1]
#[1,] 0.9976629
You will get a lot of warnings using this dataset
EDIT
You could wrap this in a function
impGlmDrop1 <- function(vars, outcome, Data=imput, Family="binomial")
{
red <- combn(vars, length(vars)-1 , simplify=FALSE)
diffs <- lapply(red, function(i) setdiff(vars, i))
full <- lapply(1:length(red), function(i)
paste(c(red[[i]], diffs[[i]]), collapse=" + "))
red <- combn(vars, length(vars)-1 , FUN=paste, collapse=" + ")
out <- vector("list", length(red))
for( i in 1:length(red)) {
redMod <- with(Data,
glm(formula(paste(outcome, red[[i]], sep="~")), family = Family))
fullMod <- with(Data,
glm(formula(paste(outcome, full[[i]], sep="~")), family = Family))
out[[i]] <- list(predictors = diffs[[i]],
pval = c(pool.compare(fullMod, redMod)$pvalue) )
}
do.call(rbind.data.frame, out)
}
# Run
impGlmDrop1(c("age", "bmi", "chl"), "(hyp==2)")