  We propose a new method to impute missing values in mixed datasets. It is
based on a principal components method, the factorial analysis for mixed data,
which balances the influence of all the variables that are continuous and
categorical in the construction of the dimensions of variability. Because the
imputation uses the principal axes and components, the prediction of the
missing values are based on the similarity between individuals and on the
relationships between variables. The quality of the imputation is assessed
through a simulation study and real datasets. The method is compared to a
recent method (Stekhoven and B\"uhlmann, 2011) based on random forests and
shows better performances especially for the imputation of categorical
variables and when there are highly linear relationships between continuous
variables.
