Missing values imputation with missMDA

[This article was first published on François Husson, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

The best thing to do with missing values is not to have any” (Gertrude Mary Cox)

Unfortunately, missing values are ubiquitous and occur for plenty of reasons. One solution is single imputation which consists in replacing missing entries with plausible values. It leads to a complete dataset that can be analyzed by any statistical methods.

Based on dimensionality reduction methods, the missMDA package successfully imputes large and complex datasets with quantitative variables, categorical variables and mixed variables. Indeed, it imputes data with principal component methods 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.

With 3 lines of code, we impute the dataset orange available in missMDA:

library(missMDA)

data(orange)

nbdim <- estim_ncpPCA(orange) # estimate the number of dimensions to impute

res.comp <- imputePCA(orange, ncp = nbdim)

In the same way, imputeMCA imputes datasets with categorical variables and imputeFAMD imputes mixed datasets.

With a completed data, we can pursue our analyses… however we need to be careful and not to forget that the data was incomplete! In a future post, we will see how to visualize the uncertainty on these predicted values.

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 enroll in this MOOC.

To leave a comment for the author, please follow the link and comment on their blog: François Husson.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)