Perform operation on each imputed dataset in R's MICE

Question

How can I perform an operation (like subsetting or adding a calculated column) on each imputed dataset in an object of class mids from R's package mice? I would like the result to still be a mids object.

Edit: Example

library(mice)
data(nhanes)

# create imputed datasets
imput = mice(nhanes)

The imputed datasets are stored as a list of lists

imput$imp

where there are rows only for the observations with imputation for the given variable.

The original (incomplete) dataset is stored here:

imput$data

For example, how would I create a new variable calculated as chl/2 in each of the imputed datasets, yielding a new mids object?

Answer 1

This can be done easily as follows -

Use complete() to convert a mids object to a long-format data.frame:

 long1 <- complete(midsobj1, action='long', include=TRUE)

Perform whatever manipulations needed:

 long1$new.var <- long1$chl/2
 long2 <- subset(long1, age >= 5)

use as.mids() to convert back manipulated data to mids object:

 midsobj2 <- as.mids(long2)

Now you can use midsobj2 as required. Note that the include=TRUE (used to include the original data with missing values) is needed for as.mids() to compress the long-formatted data properly. Note that prior to mice v2.25 there was a bug in the as.mids() function (see this post https://stats.stackexchange.com/a/158327/69413)

EDIT: According to this answer https://stackoverflow.com/a/34859264/4269699 (from what is essentially a duplicate question) you can also edit the mids object directly by accessing $data and $imp. So for example

 midsobj2<-midsobj1
 midsobj2$data$new.var <- midsobj2$data$chl/2
 midsobj2$imp$new.var <- midsobj2$imp$chl/2

You will run into trouble though if you want to subset $imp or if you want to use $call, so I wouldn't recommend this solution in general.

Answer 2

There's a function for this in the basecamb package:

library(basecamb)
apply_function_to_imputed_data(mids_object, function)

Answer 3

Another option is to calculate the variables before the imputation and place restrictions on them.

library(mice)

# Create the additional variable - this will have missing
nhanes$extra <- nhanes$chl / 2

# Change the method of imputation for extra, so that it always equals chl/2
# Change the predictor matrix so only chl predicts extra
ini <- mice(nhanes, max = 0, print = FALSE)

meth <- ini$meth
meth["extra"] <- "~I(chl / 2)"

pred <- ini$pred  # extra isn't used to predict
pred["extra", "chl"] <- 1

# Imputations
imput <- mice(nhanes, seed = 1, pred = pred, meth = meth, print = FALSE)

There are examples in mice: Multivariate Imputation by Chained Equations in R.

Answer 4

There is an overload of with that can help you here

with(imput, chl/2)

the documentation is given at ?with.mids