R: Missing Value Imputations by randomForest

rfImpute {randomForest} | R Documentation

## Missing Value Imputations by randomForest

### Description

Impute missing values in predictor data using proximity from randomForest.

### Usage

    ## Default S3 method:
    rfImpute(x, y, iter=5, ntree=300, ...)
    ## S3 method for class 'formula'
    rfImpute(x, data, ..., subset)

### Arguments

`x` | A data frame or matrix of predictors, some containing `NA`s, or a formula.
`y` | Response vector (`NA`'s not allowed).
`data` | A data frame containing the predictors and response.
`iter` | Number of iterations to run the imputation.
`ntree` | Number of trees to grow in each iteration of randomForest.
`...` | Other arguments to be passed to `randomForest`.
`subset` | A logical vector indicating which observations to use.

### Details

The algorithm starts by imputing `NA`s using `na.roughfix`. Then `randomForest` is called with the completed data. The proximity matrix from the randomForest is used to update the imputation of the `NA`s. For continuous predictors, the imputed value is the weighted average of the non-missing obervations, where the weights are the proximities. For categorical predictors, the imputed value is the category with the largest average proximity. This process is iterated `iter` times.

Note: Imputation has not (yet) been implemented for the unsupervised case. Also, Breiman (2003) notes that the OOB estimate of error from randomForest tend to be optimistic when run on the data matrix with imputed values.

### Value

A data frame or matrix containing the completed data matrix, where `NA`s are imputed using proximity from randomForest. The first column contains the response.

### Author(s)

Andy Liaw

### References

Leo Breiman (2003). Manual for Setting Up, Using, and Understanding Random Forest V4.0. https://www.stat.berkeley.edu/~breiman/Using_random_forests_v4.0.pdf

### See Also

`na.roughfix`.

### Examples

    data(iris)
    iris.na <- iris
    set.seed(111)
    ## artificially drop some data values.
    for (i in 1:4) iris.na[sample(150, sample(20)), i] <- NA
    set.seed(222)
    iris.imputed <- rfImpute(Species ~ ., iris.na)
    set.seed(333)
    iris.rf <- randomForest(Species ~ ., iris.imputed)
    print(iris.rf)

[Package randomForest version 4.6-14 Index]