The package missMDA

The package missMDA is a companion to FactoMineR that permits to handle missing values in principal component methods (PCA, CA, MCA, MFA, FAMD). It performs single and multiple imputation.

Single imputation consists in replacing missing entries with plausible values. It leads to a complete data set that can be analyzed by any statistical methods.

missMDA imputes missing values in such a way that the imputed values have no weight (i.e. have no effect and the methods is performed with only the observed values) on the PCA (or any other methods) results.

Based on dimensionality reduction methods, the missMDA package successfully imputes large and complex datasets with quantitative and/or categorical variables. Indeed, it imputes data with PCA that take into account the similarities between the observations and the relationship between variables. It has proven to be very competitive in terms of quality of the prediction compared to the state of the art methods.