Error: kNNImpute() fails if only 1 row contains missing

[mhische-md]

Hi,

in the special case that only 1 row contains missing values, kNNImpute() returns: "Error in apply(x.missing, 1, function(i) { : dim(X) must have a positive length")

As far as I can see, the sub-function impute.prelim() is responsible in line prelim = impute.prelim(x).

If more than 2 rows contain missing, this function returns a matrix for impute.prelim()$x.missing. If only 1 row contains missings, it returns a named vector, which later on can not be handled by the apply-function t(apply(x.missing, 1, function(i) {

My suggestion was to test, if inserting the drop=FALSE option at the end of function impute.prelim() solves the problem: if (byrow) x.missing = cbind(1:nrow(x), x)[missing.rows.indices, ,drop=FALSE ] else x.missing = rbind(1:ncol(x), x)[, missing.cols.indices ,drop=FALSE]

Thanks for reading :)