Multiple imputation with missMDA

missMDA generates multiple imputed data sets for continuous data using the PCA model and for categorical data using the MCA model.

Multiple imputation with PCA

See this video from 11'07 to the end.

Steps to generate 1000 imputed datasets with missMDA

  1. estimate the number of dimensions used in the reconstruction formula with the estim_ncpPCA function
  2. perform multiple imputation with the function MIPCA
  3. (optional) plot the results

Example library(missMDA)
data(orange)
nbdim = estim_ncpPCA(orange,ncp.max=5)
res.comp = MIPCA(orange, ncp = nbdim$ncp, nboot = 1000)
plot(res.comp)

It is then possible to plot the results and thus visualize the uncertainties of the imputed values (see this section for more information).

Multiple imputation with MCA

In the same way, multiple imputation can be done for qualitative data sets thanks to MCA.

Steps to generate imputed datasets with missMDA

  1. estimate the number of dimensions used in the reconstruction formula with the estim_ncpMCA function
  2. perform multiple imputation with the function MIPCA
  3. (optional) plot the results to visualize the uncertainties

Example library(missMDA)
data(vnf)
nbdim = estim_ncpMCA(vnf,ncp.max=5) ## Time-consuming, nb = 4
res = MIMCA(vnf, ncp=4,nboot=10)
plot(res)