Multiple Imputation using Chained Random Forests


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Documentation for package ‘RfEmpImp’ version 2.0.3

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gen.mcar Generate missing (completely at random) cells in the dataset
imp.rfemp Perform multiple imputation based on the empirical error distribution of random forests
imp.rfnode.cond Perform multiple imputation based on the conditional distribution formed by prediction nodes of random forests
imp.rfnode.prox Multiple imputation using chained random forests and node proximities
mice.impute.rfemp Multiple imputation for categorical variables based on predictions of random forest
mice.impute.rfnode Sampling function for multiple imputation based on predicting nodes of random forests
mice.impute.rfnode.cond Sampling function for multiple imputation based on predicting nodes of random forests
mice.impute.rfnode.prox Sampling function for multiple imputation based on predicting nodes of random forests
mice.impute.rfpred.cate Multiple imputation for categorical variables based on predictions of random forest
mice.impute.rfpred.emp Multiple imputation using chained random forests: RfPred.Emp
mice.impute.rfpred.norm Multiple imputation using chained random forests: RfPred.Norm
reg.ests Get regression estimates for pooled object