randomForestSRC (version 1.6.1)

impute.rfsrc: Impute Only Mode

Description

Fast imputation mode. A random forest is grown and used to impute missing data. No ensemble estimates or error rates are calculated.

Usage

## S3 method for class 'rfsrc':
impute(formula, data, ntree = 250, mtry = NULL,
  nodesize = 1, splitrule = NULL, nsplit = 1,
  na.action = c("na.impute", "na.random"),
  nimpute = 1, 
  xvar.wt = NULL, 
  do.trace = FALSE, ...)

Arguments

formula

A symbolic description of the model to be fit. Can be left unspecified if there are no outcomes or we don't care to distinguish between y-outcomes and x-variables in the imputation.

data

Data frame containing the data to be imputed.

ntree

Number of trees to grow.

mtry

Number of variables randomly sampled at each split.

nodesize

Minimum terminal node size.

splitrule

Splitting rule used to grow trees.

nsplit

Non-negative integer value used to specify random splitting.

na.action

Missing value action. See details below.

nimpute

Number of iterations of missing data algorithm.

xvar.wt

Weights for selecting variables for splitting on.

do.trace

Logical. Should trace output be enabled on each iteration? Default is FALSE.

...

Further arguments passed to or from other methods.

Value

• Invisibly, the data frame containing the orginal data with imputed data overlayed.

Details

1. Grow a forest and use this to impute data. All external calculations such as ensemble calculations, error rates, etc. are turned off. Use this function if your only interest is imputing the data.
2. Data is imputed using the default missing data algorithm, however users can selectna.action="na.random"for a cruder, but faster imputation. Unlikena.action="na.impute", data is not imputed as the tree is grown, instead tree nodes are split using non-missing in-bag data. Following the split to a node, data points with missing values on the variable used to split the node are randomly assigned to daughter nodes.
3. If no formula is specified, unsupervised splitting is implemented which treats the data as if there are no y-outcomes.
4. Prior to imputation, the data is processed and records with all values missing are removed, as are variables having all missing values.
5. If there is no missing data, either before or after processing of the data, the algorithm returns the processed data and no imputation is performed.
6. All options are the same asrfsrcand the user should consult therfsrchelp file for details.

References

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests, Ann. App. Statist., 2:841-860.

Stekhoven D.J. and Buhlmann P. (2012). MissFores--non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1):112-118.

See Also

rfsrc

Examples

## ------------------------------------------------------------
## example of survival imputation
## ------------------------------------------------------------

#imputation using outcome splitting
data(pbc, package = "randomForestSRC")
pbc.d <- impute.rfsrc(Surv(days, status) ~ ., data = pbc, nsplit = 3)

#when no formula is given we default to unsupervised splitting
pbc2.d <- impute.rfsrc(data = pbc, nodesize = 1, nsplit = 10, nimpute = 5)

#random splitting can be reasonably good
pbc3.d <- impute.rfsrc(Surv(days, status) ~ ., data = pbc,
          splitrule = "random", nodesize = 1, nimpute = 5)

## ------------------------------------------------------------
## example of regression imputation
## ------------------------------------------------------------

air.d <- impute.rfsrc(Ozone ~ ., data = airquality, nimpute = 5)
air2.d <- impute.rfsrc(data = airquality, nimpute = 5, nodesize = 1)
air3.d <- impute.rfsrc(Ozone ~ ., data = airquality, nimpute = 5,
           splitrule = "random", nodesize = 1)