Imputation by Tricube Predictive Mean Matching
This function performs tricube predictive mean matching (see
Hmisc::aregImpute)
in which donors are weighted according to distances of predicted values.
Three donors are chosen.
mice.impute.tricube.pmm(y, ry, x, tricube.pmm.scale=0.2, tricube.boot=FALSE, ...)
y |
Incomplete data vector of length |
ry |
Vector of missing data pattern ( |
x |
Matrix ( |
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 |
A vector of length nmis=sum(!ry) with imputed values.
Hmisc::aregImpute
## 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.pmm", 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 imputation method 'tricube.pmm' 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 )
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