# mice.impute.tricube.pmm: Imputation by Tricube Predictive Mean Matching In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

## Description

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

## Usage

 ```1 2 3``` ```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.

## Note

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

## Author(s)

Alexander Robitzsch

`Hmisc::aregImpute`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```## 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 <- miceadds::mice.1chain( nhanes , burnin=5 , iter=20 , Nimp=4 , imputationMethod= iM2 , tricube.pmm.scale=tricube.pmm.scale1 ) ## End(Not run) ```