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miceRanger (version 1.3.4)

Multiple Imputation by Chained Equations with Random Forests

Description

Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) . Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann 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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Install

install.packages('miceRanger')

Monthly Downloads

433

Version

1.3.4

License

MIT + file LICENSE

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Maintainer

Samuel Wilson

Last Published

February 22nd, 2020

Functions in miceRanger (1.3.4)

getVarImps

Get Variable Imputations
sampleMiceDefs

Sample miceDefs object built off of iris dataset. Included so examples don't run for too long.
impute

Impute New Data With Existing Models
plotDistributions

plotDistributions
plotImputationVariance

plotImputationVariance
plotModelError

plotModelError
plotVarConvergence

plotVarConvergence
addIterations

addIterations
addDatasets

addDatasets
plotCorrelations

plotCorrelations
miceRanger

miceRanger: Fast Imputation with Random Forests
print.miceDefs

Print a miceDefs object
plotVarImportance

plotVarImportance
amputeData

amputeData
completeData

completeData