# Dealing with a Heywood Case in R

I am running a principal components analysis in R using the principal function. When I run my data using varimax rotation (orthogonal) I do not have a problem. However, if I run my data using oblimin rotation (oblique) R gives me a warning that a Heywood case was detected (two of the variables have a loading that is greater than 1). I can't seem to find any documentation on what to do about it. I thought this was more of an issue with factor analysis and that PCA didn't have such issues, but I suppose I am wrong. Is there a way to best handle this issue?