I am trying to compare imputation methods for an 81 samples x 407 variables data set with ~17% missing values. Some of the variables will be correlated, some highly, that is the nature of the data. I have already filtered variables out of the data set according to variance in quality control samples and number of missing values within treatment groups. None of the variables are categorical, all are numeric or integers. My script is:
mice(data, m=5, method="pmm")
And the error is:
Error in solve.default(xtx + diag(pen)) : system is computationally singular: reciprocal condition number = 1.48341e-18
Other imputation functions work just fine i.e. median, k nearest neighbors, random forest. Why does mice fail?