Impute missing values in predictor data using proximity from RRF.

```
# S3 method for default
rrfImpute(x, y, iter=5, ntree=300, ...)
# S3 method for formula
rrfImpute(x, data, ..., subset)
```

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 RRF.

...

Other arguments to be passed to
`RRF`

.

subset

A logical vector indicating which observations to use.

A data frame or matrix containing the completed data matrix, where
`NA`

s are imputed using proximity from RRF. The first
column contains the response.

The algorithm starts by imputing `NA`

s using
`na.roughfix`

. Then `RRF`

is called
with the completed data. The proximity matrix from the RRF
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 RRF tend to be optimistic when run on the data matrix with imputed values.

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

# NOT RUN { 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 <- rrfImpute(Species ~ ., iris.na) set.seed(333) iris.rf <- RRF(Species ~ ., iris.imputed) print(iris.rf) # }