I have a data set which has missing values on several columns. The analysis I am doing involves regressions where several of the variables are used as dependent variables, and others as explanatory variables.
For multiple imputation, the advice that I have read (e.g. Wulff, J. N., & Ejlskov, L. (2017). Multiple Imputation by Chained Equations in Praxis: Guidelines and Review) suggests that the DVs should be included in the imputation model. So I created a model where the DVs are included as predictors, but not imputed.
However, because the DVs have missing values themselves, the imputed explanatory variables end up still with some missing values, I assume because the DVs are being used as predictors in the imputation model. If I remove the DVs from the imputation model, I get (almost) no missing values for the imputed explanatory variables.
What is the correct way to handle this situation? Should I remove the DVs from the imputation model (contra Wulff & Ejlskov, and others)? Or should I impute the DVs as well, and if so, should I use the imputed DV values in the regressions?
Here is some R code to illustrate the problem:
library(mice) library(missForest) set.seed(123) # Create a dataset with missing values on 2 columns iris.mis = prodNA(iris[, c('Sepal.Length', 'Petal.Length')], 0.20) iris.mis = cbind(iris.mis, iris[, c('Sepal.Width', 'Petal.Width', 'Species')]) # Setup MICE init = mice(iris.mis, maxit = 0) iris.metod = init$method iris.method['Sepal.Length'] = "" # Do not impute Sepal.Length # Run MICE imp = mice(iris.mis, m = 5, maxit = 5, method = iris.method) # Inspect Result res = mice::complete(imp, 1) print(length(res$Petal.Length[is.na(res$Petal.Length)])) #  8 # i.e. still 8 missing values in Petal.Length, when we wanted 0