Categorical data

Multiple imputation for continuous and categorical data

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“The idea of imputation is both seductive and dangerous” (R.J.A Little & D.B. Rubin).

Indeed, a predicted value is considered as an observed one and the uncertainty of prediction is ignored, conducting to bad inferences with missing values. That is why Multiple Imputation is recommended.

The missMDA package quickly generates several imputed datasets with quantitative variables and/or categorical variables. It is based on dimensionality reduction methods such as PCA for continuous variables or multiple correspondence analysis for categorical variables. Compared to the packages Amelia and mice, it better handles cases where the number of variables is larger than the number of units, and cases where regularization is needed (i.e. when the imputation model is prone to overfitting issues). For categorical variables, it is particularly interesting with many variables and many levels, but also with rare levels.

With 3 lines of code, we generate 1000 imputed datasets for the quantitative orange data available in missMDA:

library(missMDA)
data(orange)
nbdim <- estim_ncpPCA(orange) # estimate the number of dimensions to impute
res.comp <- MIPCA(orange, ncp = nbdim$ncp, nboot = 1000)

In the same way, MIMCA can be used for categorical data:

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

You can find more information in this JSS paper, on this website, on this tutorial given at useR!2016 at stanford.

You can also watch this playlist on Youtube to practice with R.

You can also contact us:
julie.josse@polytechnique.edu       @JulieJosseStat
husson@agrocampus-ouest.fr

Multiple Correspondence Analysis with FactoMineR

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How to analyse of categorical data? Here is a course with videos that present Multiple Correspondence Analysis in a French way. The most well-known use of Multiple Correspondence Analysis is: surveys.

Four videos present a course on MCA, highlighting the way to interpret the data. Then  you will find videos presenting the way to implement MCA in FactoMineR, to deal with missing values in MCA thanks to the package missMDA and lastly a video to draw interactive graphs with Factoshiny. And finally you will see that the new package FactoInvestigate allows you to obtain automatically an interpretation of your MCA results.

With this course, you will be stand-alone to perform and interpret results obtain with MCA.

MCA4

 

For more information, you can see the book blow. Here are some reviews on the book and a link to order the book.

bookR