mice.impute.tricube.pmm: Imputation by Tricube Predictive Mean Matching

Description Usage Arguments Value Note See Also Examples

View source: R/mice.impute.tricube.pmm.R

Description

This function performs tricube predictive mean matching (see {Hmisc::aregImpute}) in which donors are weighted according to distances of predicted values.

Usage

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mice.impute.tricube.pmm(y, ry, x, tricube.pmm.scale=0.2, tricube.boot=FALSE, ...)

mice.impute.tricube.pmm2(y, ry, x, tricube.pmm.scale=0.2, tricube.boot=FALSE, ...)

Arguments

y

Incomplete data vector of length n

ry

Vector of missing data pattern (FALSE – missing, TRUE – observed)

x

Matrix (n x p) of complete covariates.

tricube.pmm.scale

A scaling factor for tricube matching. The default is 0.2.

tricube.boot

A logical indicating whether tricube matching should be performed using a bootstrap sample

...

Further arguments to be passed

Value

A vector of length nmis=sum(!ry) with imputed values.

Note

The imputation method tricube.pmm2 is usually somewhat faster than tricube.pmm.

See Also

{Hmisc::aregImpute}

Examples

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## Not run: 
#############################################################################
# EXAMPLE 1: Tricube predictive mean matching for nhanes data
#############################################################################

library(mice)
data(nhanes, package="mice")
set.seed(9090)

#*** Model 1: Use default of tricube predictive mean matching
varnames <- colnames(nhanes)
VV <- length(varnames)
method <- rep("tricube.pmm2", VV )
names(method) <- varnames
# imputation with mice
imp.mi1 <- mice::mice( nhanes, m=5, maxit=4, method=method )

#*** Model 2: use item-specific imputation methods
iM2 <- method
iM2["bmi"] <- "pmm6"
# use tricube.pmm2 for hyp and chl
# select different scale parameters for these variables
tricube.pmm.scale1 <- list( "hyp"=.15, "chl"=.30 )
imp.mi2 <- miceadds::mice.1chain( nhanes, burnin=5, iter=20, Nimp=4,
    method=iM2, tricube.pmm.scale=tricube.pmm.scale1  )

## End(Not run)

alexanderrobitzsch/miceadds documentation built on Oct. 21, 2018, 8:28 p.m.