R/imputation.R

In rrcovNA: Scalable Robust Estimators with High Breakdown Point for Incomplete Data

.imputation <- function(x, impMeth = c("norm" , "seq", "rseq"), alpha=0.5)
{

    impMeth <- match.arg(impMeth)

    if(is.data.frame(x))
        x <- data.matrix(x)
    else if (!is.matrix(x))
        x <- matrix(x, length(x), 1,
                    dimnames = list(names(x), deparse(substitute(x))))

    ## drop all rows which contain only missings
    na.x <- rowSums(ifelse(is.na(x),1,0)) == ncol(x)
    ok <- !na.x
    x <- x[ok, , drop = FALSE]

    dx <- dim(x)
    dimn <- dimnames(x)
    n <- dx[1]
    p <- dx[2]

    if(impMeth == "norm")
    {
        ## impute the missing data using package norm
        s <- prelim.norm(x)                     # do preliminary manipulations
        thetahat <- em.norm(s, showits=FALSE)   # find the mle
        rngseed(1234567)                        # set random number generator seed
        ximp <- imp.norm(s, thetahat, x)        # impute missing data under the MLE
    }else if(impMeth == "seq")
    {
        ximp <- impSeq(x)
    }else if(impMeth == "rseq")
    {
        ximp <- impSeqRob(x, alpha=alpha)$x
    }else
        stop("Undefined imputation method in .cov.na.imp(): ", impMeth)

    return(ximp)
}