R/mice.impute.hotDeck.R

In alexanderrobitzsch/miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

## File Name: mice.impute.hotDeck.R
## File Version: 0.13

mice.impute.hotDeck <- function (y, ry, x, donors=5, method="Mahalanobis", ...)
{
    require_namespace(pkg="sirt")
    N1 <- sum(ry)
    N0 <- sum(!ry)
    #*** center x variables
    x <- ma.scale2(x)
    ry2 <- sort(sample( which(ry), N1, replace=TRUE ))
    X1 <- x[ ry2,,drop=FALSE]
    X0 <- x[ !ry,, drop=FALSE]
    NV <- ncol(X1)
    #*** create distance matrix
    distmat <- matrix( NA, nrow=N0, ncol=N1 )
    #--- hot deck using Mahalanobis distance
    if (method=="Mahalanobis"){
        if (NV>1){
            W <- solve( stats::cov(X1) )
        } else {
            W <- matrix(1, nrow=1,ncol=1)
        }
        for (nn in 1:N0){
            X0_nn <- matrix( X0[nn,], nrow=N1, ncol=NV, byrow=TRUE )
            Xdist_nn <- X0_nn - X1
            d1 <- Xdist_nn %*% W
            distmat[nn,] <- rowSums( d1 * Xdist_nn )
        }
    }
    #--- hot deck using weights from correlations
    if (method %in% c("cor","lm") ){
        if (method=="cor"){
            W0 <- as.vector( stats::cor( y[ry2], X1 ) )
        }
        if (method=="lm"){
            mod1 <- stats::lm( y[ry2] ~ X1 )
            W0 <- coef(mod1)[-1]
        }
        W0 <- matrix( abs(W0), nrow=N1, ncol=NV, byrow=TRUE)
        for (nn in 1:N0){
            X0_nn <- matrix( X0[nn,], nrow=N1, ncol=NV, byrow=TRUE )
            Xdist_nn <- X0_nn - X1
            distmat[nn,] <- rowSums( W0 * Xdist_nn^2 )
        }
    }
    #***** matching
    m1 <- sirt::rowKSmallest2.sirt(matr=distmat, K=donors )$smallind
    g1 <- m1[ cbind( 1:N0, sample( 1:donors, N0, replace=TRUE ) ) ]
    #*** imputed values
    yimp <- y[ry2][g1]
    #--- output
    return(yimp)
}