impute: Impute New Data With Existing Models
In miceRanger: Multiple Imputation by Chained Equations with Random Forests

Description Usage Arguments Details Value Examples

Impute data using the information from an existing miceDefs object.

impute(
  data,
  miceObj,
  datasets = 1:miceObj$callParams$m,
  iterations = miceObj$callParams$maxiter,
  verbose = TRUE
)

`data`	The data to be imputed. Must have all columns used in the imputation of miceDefs.
`miceObj`	A miceDefs object created by `miceRanger()`.
`datasets`	A numeric vector specifying the datasets with which to impute `data`. See details for more information.
`iterations`	The number of iterations to run. By default, the same as the number of iterations currently in `miceObj`.
`verbose`	should progress be printed?

This capability is experimental, but works well in benchmarking. The original data and random forests (if returnModels = TRUE) are returned when miceRanger is called. These models can be recycled to impute a new dataset in the same fashion as miceRanger, by imputing each variable over a series of iterations. Each dataset created in miceObj can be thought of as a different imputation mechanism, with different initialized values and a different associated random forests. Therefore, it is necessary to choose the datasets which will be used to impute the data. When mean matching a numeric variable, the candidate values are drawn from the original data passed to miceRanger, not the data passed to this function.

An object of class impDefs, which contains information about the imputation process.

`callParams`	The parameters of the object.
`data`	The original data provided by the user.
`naWhere`	Logical index of missing data, having the same dimensions as `data`.
`missingCounts`	The number of missing values for each variable.
`imputedData`	A list of imputed datasets.

ampDat <- amputeData(iris)
miceObj <- miceRanger(ampDat,1,1,returnModels=TRUE,verbose=FALSE)

newDat <- amputeData(iris)
newImps <- impute(newDat,miceObj)