mice.impute.tricube.pmm.Rd
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.
if (FALSE) { ############################################################################# # 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 ) }