na.roughfix: Rough Imputation of Missing Values

In randomForest: Breiman and Cutler's random forests for classification and regression

Description

Impute Missing Values by median/mode.

Usage

na.roughfix(object, ...)

Arguments

object	a data frame or numeric matrix.
...	further arguments special methods could require.

Value

A completed data matrix or data frame. For numeric variables, NAs are replaced with column medians. For factor variables, NAs are replaced with the most frequent levels (breaking ties at random). If object contains no NAs, it is returned unaltered.

Note

This is used as a starting point for imputing missing values by random forest.

Author(s)

Andy Liaw

See Also

rfImpute, randomForest.

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
iris.roughfix <- na.roughfix(iris.na)
iris.narf <- randomForest(Species ~ ., iris.na, na.action=na.roughfix)
print(iris.narf)

Example output

randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.

Call:
 randomForest(formula = Species ~ ., data = iris.na, na.action = na.roughfix) 
               Type of random forest: classification
                     Number of trees: 500
No. of variables tried at each split: 2

        OOB estimate of  error rate: 4.67%
Confusion matrix:
           setosa versicolor virginica class.error
setosa         50          0         0        0.00
versicolor      0         46         4        0.08
virginica       0          3        47        0.06

randomForest documentation built on May 2, 2019, 5:54 p.m.