rrp.impute: Nearest neighbor hot-deck imputation using RRP dissimilarity...
In rrp: Random Recursive Partitioning

Description Usage Arguments Details Value Author(s) References See Also Examples

This function performs a simple nearest neighbor hot-deck imputation method using the RRP dissimilarity matrix.

1	rrp.impute(data, D = NULL, k = 1, msplit = 10, Rep = 250, cut.in = 15)

`data`	a `data.frame` containing missing data on some covariates
`D`	`NULL` or an object of class `XPtr`
`k`	number of nearest neighbors to use
`msplit`	minimum split parameter in the `rpart` algorithm
`Rep`	number of RRP replications
`cut.in`	number of breaks used to cut continuous covariates

If missing data are on a continuous covariate, the missing value is imputed as the average of the covariate values of the nearest neighbors, otherwise the majority of the ‘votes’ determines the class of the missing observation on the basis of nearest available data.

If D is NULL a RRP-dissimilarity matrix is created.

From version 1.6 of the package the RRP matrix is stored as an external pointer to avoid duplications. This allow to work on bigger datasets. Hence this function no longer accepts dist objects.

A list

`new.data`	a copy of the data `data` with missing data imputed
`dist`	an object of class `XPtr` used to search for nearest neighbors

S.M. Iacus

Iacus, S.M., Porro, G. (2009) Random Recursive Partitioning: a matching method for the estimation of the average treatment effect, Journal of Applied Econometrics, 24, 163-185.

Iacus, S.M., Porro, G. (2007) Missing data imputation, matching and other applications of random recursive partitioning, Computational Statistics and Data Analysis, 52, 2, 773-789.

rrp.dist, rrp.class

data(iris)

X <- iris
n <- dim(X)[1]

set.seed(123)
miss <- sample(1:n, 10)
for(i in miss)
 X[i, sample(1:5, 2)] <- NA
 
X[miss,] 

## unsupervised
x <- rrp.impute(X)

x$new.data[miss,]
iris[miss,]