miceRanger v1.3.4

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Multiple Imputation by Chained Equations with Random Forests

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.

Functions in miceRanger

Name Description
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
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Vignettes of miceRanger

Name
MICEalgorithm.png
PMM.png
diagnosticPlotting.Rmd
miceAlgorithm.Rmd
mmEffectsCloseBimodal.png
mmEffectsFarBimodal.png
mmEffectsInteger.png
mmEffectsSkewed.png
usingMiceRanger.Rmd
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Details

URL https://github.com/FarrellDay/miceRanger
BugReports https://github.com/FarrellDay/miceRanger/issues
Encoding UTF-8
LazyData true
License MIT + file LICENSE
RoxygenNote 7.0.2
VignetteBuilder knitr
NeedsCompilation no
Packaged 2020-02-22 01:46:57 UTC; SamWilson
Repository CRAN
Date/Publication 2020-02-22 05:50:02 UTC

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