| rfImpute {randomForest} | R Documentation |
Impute missing values in predictor data using proximity from randomForest.
## Default S3 method: rfImpute(x, y, iter=5, ntree=300, ...) ## S3 method for class 'formula': rfImpute(x, data, ..., subset)
x |
A data frame or matrix of predictors, some containing
NAs, 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. |
The algorithm starts by imputing NAs 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 NAs. 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.
A data frame or matrix containing the completed data matrix, where
NAs are imputed using proximity from randomForest. The first
column contains the response.
Andy Liaw
Leo Breiman (2003). Manual for Setting Up, Using, and Understanding Random Forest V4.0. http://oz.berkeley.edu/users/breiman/Using_random_forests_v4.0.pdf
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)