miceadds (version 2.2-0)

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

Description

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

Usage

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 traicube 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.

See Also

Hmisc::aregImpute

Examples

## 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)
# imputationMethod <- rep("tricube.pmm2" , VV )
# names(imputationMethod) <- varnames
# # imputation with mice
# imp.mi1 <- mice::mice( nhanes , m=5 , maxit=4 , imputationMethod= imputationMethod )
# 
# #*** Model 2: use item-specific imputation methods
# iM2 <- imputationMethod
# 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 <- mice.1chain( nhanes , burnin=5 , iter=20 , Nimp=4 ,
#     imputationMethod= iM2 , tricube.pmm.scale=tricube.pmm.scale1  )
# ## End(Not run)