Multiple Imputation by Chained Equations with Random Forests
addDatasets
addIterations
amputeData
completeData
Get Variable Imputations
Impute New Data With Existing Models
miceRanger: Fast Imputation with Random Forests
plotCorrelations
plotDistributions
plotImputationVariance
plotModelError
plotVarConvergence
plotVarImportance
Print a miceDefs object
Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) <doi:10.1177/0962280206074463>. Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann <arXiv:1105.0828> to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel.
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