Multiple Imputation using Chained Random Forests

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Documentation for package ‘RfEmpImp’ version 2.1.8

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

conv.factor	Convert variables to factors
gen.mcar	Generate missing (completely at random) cells in a data set
imp.rfemp	Perform multiple imputation using the empirical error distributions and predicted probabilities of random forests
imp.rfnode.cond	Perform multiple imputation based on the conditional distribution formed by prediction nodes of random forests
imp.rfnode.prox	Perform multiple imputation based on the conditional distribution formed using node proximity
mice.impute.rfemp	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 forests
mice.impute.rfnode	Univariate sampler function for mixed types of variables for node-based imputation, using predicting nodes of random forests
mice.impute.rfnode.cond	Univariate sampler function for mixed types of variables for node-based imputation, using predicting nodes of random forests
mice.impute.rfnode.prox	Univariate sampler function for mixed types of variables for node-based imputation, using predicting nodes of random forests
mice.impute.rfpred.cate	Univariate sampler function for categorical variables for prediction-based imputation, using predicted probabilities of random forest
mice.impute.rfpred.emp	Univariate sampler function for continuous variables using the empirical error distributions
mice.impute.rfpred.norm	Univariate sampler function for continuous variables for prediction-based imputation, assuming normality for prediction errors of random forest
query.rf.pred.idx	Identify corresponding observations indexes under the terminal nodes for a random forest model by 'ranger'
query.rf.pred.val	Identify corresponding observed values for the response variable under the terminal nodes for a random forest model by 'ranger'
rangerCallerSafe	Remove unnecessary arguments for 'ranger' function
reg.ests	Get regression estimates for pooled object