R - mice - machine learning: re-use imputation scheme from train to test set

Question

I'm building a predictive model and am using the mice package for imputing NAs in my training set. Since I need to re-use the same imputation scheme for my test set, how can I re-apply it to my test data?

# generate example data
set.seed(333)
mydata <- data.frame(a = as.logical(rbinom(100, 1, 0.5)),
                     b = as.logical(rbinom(100, 1, 0.2)),
                     c = as.logical(rbinom(100, 1, 0.8)),
                     y = as.logical(rbinom(100, 1, 0.6)))

na_a <- as.logical(rbinom(100, 1, 0.3))
na_b <- as.logical(rbinom(100, 1, 0.3))
na_c <- as.logical(rbinom(100, 1, 0.3))
mydata$a[na_a] <- NA
mydata$b[na_b] <- NA
mydata$c[na_c] <- NA

# create train/test sets
library(caret)
inTrain <- createDataPartition(mydata$y, p = .8, list = FALSE)
train <- mydata[ inTrain, ] 
test <-  mydata[-inTrain, ]

# impute NAs in train set
library(mice)
imp <- mice(train, method = "logreg")
train_imp <- complete(imp)

# apply imputation scheme to test set
test_imp <- unknown_function(test, imp$unknown_data)

Answer 1

prockenschaub has created a lovely function for that, called mice.reuse()

library(mice)
library(scorecard)

# function to impute new observations based on the previous imputation model
source("https://raw.githubusercontent.com/prockenschaub/Misc/master/R/mice.reuse/mice.reuse.R")

# split data into train and test
data_list <- split_df(airquality, y = NULL, ratio = 0.75, seed = 186)

imp <- mice(data = data_list$train, 
            seed = 500, 
            m = 5,
            method = "pmm",
            print = FALSE)


# impute test data based on train imputation model
test_imp <- mice.reuse(imp, data_list$test, maxit = 1)

Answer 2

As of mice::mice version 3.12.0 contains the ignore parameter which will cover most use cases.

Simply pass it a vector with TRUE for all rows that should be used during training and FALSE for all rows that should only be imputed (but not used during training).

imp.ignore <- mice(data, ignore = c(rep(FALSE, 99), TRUE), maxit = 5, m = 2, seed = 1)