Package: RfEmpImp 2.1.8

RfEmpImp: Multiple Imputation using Chained Random Forests

An R package for multiple imputation using chained random forests. Implemented methods can handle missing data in mixed types of variables by using prediction-based or node-based conditional distributions constructed using random forests. For prediction-based imputation, the method based on the empirical distribution of out-of-bag prediction errors of random forests and the method based on normality assumption for prediction errors of random forests are provided for imputing continuous variables. And the method based on predicted probabilities is provided for imputing categorical variables. For node-based imputation, the method based on the conditional distribution formed by the predicting nodes of random forests, and the method based on proximity measures of random forests are provided. More details of the statistical methods can be found in Hong et al. (2020) <arxiv:2004.14823>.

Authors:Shangzhi Hong [aut, cre], Henry S. Lynn [ths]

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RfEmpImp/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/shangzhi-hong/rfempimp/issues

On CRAN:

imputationmissing-datarandom-forest

15 exports 5 stars 1.10 score 61 dependencies 199 downloads

Last updated 2 years agofrom:f5898e3e53

Exports:conv.factorgen.mcarimp.rfempimp.rfnode.condimp.rfnode.proxmice.impute.rfempmice.impute.rfnodemice.impute.rfnode.condmice.impute.rfnode.proxmice.impute.rfpred.catemice.impute.rfpred.empmice.impute.rfpred.normquery.rf.pred.idxquery.rf.pred.valreg.ests

Dependencies:backportsbitbit64bootbroomclicliprcodetoolscpp11crayondplyrfansiforcatsforeachgenericsglmnetgluehavenhmsiteratorsjomolatticelifecyclelme4magrittrMASSMatrixmiceminqamitmlnlmenloptrnnetnumDerivordinalpanpillarpkgconfigprettyunitsprogresspurrrR6rangerRcppRcppEigenreadrrlangrpartshapestringistringrsurvivaltibbletidyrtidyselecttzdbucminfutf8vctrsvroomwithr

Introduction to RfEmpImp

Rendered fromIntroduction.Rmdusingknitr::rmarkdownon Jul 26 2024.

Last update: 2020-06-02
Started: 2020-05-18

Readme and manuals

Help Manual

Help pageTopics
Convert variables to factorsconv.factor
Generate missing (completely at random) cells in a data setgen.mcar
Perform multiple imputation using the empirical error distributions and predicted probabilities of random forestsimp.rfemp
Perform multiple imputation based on the conditional distribution formed by prediction nodes of random forestsimp.rfnode.cond
Perform multiple imputation based on the conditional distribution formed using node proximityimp.rfnode.prox
Univariate sampler function for mixed types of variables for prediction-based imputation, using empirical distribution of out-of-bag prediction errors and predicted probabilities of random forestsmice.impute.rfemp
Univariate sampler function for mixed types of variables for node-based imputation, using predicting nodes of random forestsmice.impute.rfnode mice.impute.rfnode.cond mice.impute.rfnode.prox
Univariate sampler function for categorical variables for prediction-based imputation, using predicted probabilities of random forestmice.impute.rfpred.cate
Univariate sampler function for continuous variables using the empirical error distributionsmice.impute.rfpred.emp
Univariate sampler function for continuous variables for prediction-based imputation, assuming normality for prediction errors of random forestmice.impute.rfpred.norm
Identify corresponding observations indexes under the terminal nodes for a random forest model by 'ranger'query.rf.pred.idx
Identify corresponding observed values for the response variable under the terminal nodes for a random forest model by 'ranger'query.rf.pred.val
Remove unnecessary arguments for 'ranger' functionrangerCallerSafe
Get regression estimates for pooled objectreg.ests