Package: miceRanger 1.5.1

Sam Wilson

miceRanger: 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.

Authors:Sam Wilson [aut, cre]

miceRanger_1.5.1.tar.gz
miceRanger_1.5.1.zip(r-4.7-any)miceRanger_1.5.1.zip(r-4.6-any)miceRanger_1.5.1.zip(r-4.5-any)
miceRanger_1.5.1.tgz(r-4.6-any)miceRanger_1.5.1.tgz(r-4.5-any)
miceRanger_1.5.1.tar.gz(r-4.7-any)miceRanger_1.5.1.tar.gz(r-4.6-any)
miceRanger_1.5.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
miceRanger/json (API)

# Install 'miceRanger' in R:
install.packages('miceRanger', repos = c('https://farrellday.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/farrellday/miceranger/issues

Datasets:
  • sampleMiceDefs - Sample miceDefs object built off of iris dataset. Included so examples don't run for too long.

On CRAN:

Conda:

imputation-methodsmachine-learningmicemissing-datamissing-valuesrandom-forests

7.74 score 70 stars 2 packages 87 scripts 561 downloads 13 exports 116 dependencies

Last updated from:4b87a65189. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE203
source / vignettesOK233
linux-releaseNOTE143
macos-releaseNOTE239
macos-oldrelNOTE182
windows-develNOTE121
windows-releaseNOTE150
windows-oldrelNOTE116
wasm-releaseOK108

Exports:addDatasetsaddIterationsamputeDatacompleteDatagetVarImpsimputemiceRangerplotCorrelationsplotDistributionsplotImputationVarianceplotModelErrorplotVarConvergenceplotVarImportance

Dependencies:abindaskpassbackportsbitbit64bootbroomcarcarDatacellrangerclassclicliprcodetoolscolorspacecorrplotcowplotcpp11crayoncurldata.tableDerivDescToolsdoBydplyre1071ExactexpmfarverFNNforcatsforeachforecastFormulafracdifffsgenericsggplot2ggpubrggrepelggsciggsignifgldgluegridExtragtablehavenhmshttrisobanditeratorsjsonlitelabelinglatticelifecyclelme4lmomlmtestmagrittrMASSMatrixMatrixModelsmgcvmimeminqamodelrmvtnormnlmenloptrnnetnumDerivopensslpbkrtestpillarpkgconfigpolynomprettyunitsprogressproxypurrrquantregR6rangerrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrreadxlreformulasrematchrlangrootSolverstatixrstudioapiS7scalesSparseMstringistringrsurvivalsystibbletidyrtidyselecttimeDatetzdburcautf8vctrsviridisLitevroomwithrzoo

Diagnostic Plotting
Setup | Distribution of Imputed Values | Convergence of Correlation | Center and Dispersion Convergence | Model OOB Error | Variable Importance | Imputed Variance Between Datasets
Rendered fromdiagnosticPlotting.Rmdusingknitr::rmarkdown

Last update: 2020-02-17
Started: 2020-01-23

Imputing Missing Data with miceRanger
Introduction | Using miceRanger | Simple example | Running in Parallel | Adding More Iterations/Datasets | Specifying Predictors, Value Selector, and Mean Matching Candidates by Variable | Imputing New Data with Existing Models | Using the Imputed Data
Rendered fromusingMiceRanger.Rmdusingknitr::rmarkdown

Last update: 2020-02-17
Started: 2020-01-09

The MICE Algorithm
Introduction | Predictive Mean Matching | Effects of Mean Matching | Bimodial Variable | Skewed Variable | Integer Variable | Common Use Cases of MICE | Data Leakage: | Funnel Analysis: | Confidence Intervals:
Rendered frommiceAlgorithm.Rmdusingknitr::rmarkdown

Last update: 2020-02-17
Started: 2020-01-23