missMDA: A Package for Handling Missing Values in Multivariate Data Analysis

By Julie Josse, François Husson. 2016

1,155 citations recorded by the source corpus.

We present the R package missMDA which performs principal component methods on incomplete data sets, aiming to obtain scores, loadings and graphical representations despite missing values. Package methods include principal component analysis for continuous variables, multiple correspondence analysis for categorical variables, factorial analysis on mixed data for both continuous and categorical variables, and multiple factor analysis for multi-table data. Furthermore, missMDA can be used to perform single imputation to complete data involving continuous, categorical and mixed variables. A multiple imputation method is also available. In the principal component analysis framework, variability across different imputations is represented by confidence areas around the row and column positions on the graphical outputs. This allows assessment of the credibility of results obtained from incomplete data sets.

Subfields: Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/16390685beeadd99f7ef2564d83b54437ed04c62

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Food Science

1. Matthias Templ
2. Alexander Kowarik
3. Jes Frellsen
4. Pierre-Alexandre Mattei

In Statistics and Probability

1. Jes Frellsen
2. Pierre-Alexandre Mattei
3. Matthias Templ
4. Alexander Kowarik

Citations in the corpus

• MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets (2019, methodology): “…(2011) (fit using the missForest R package with default options); a technique based on principal component analysis (PCA, Josse & Husson, 2012a), fit using the missMDA R package (Josse & Husson, 2016), where the number of principal components is found using cross validation (Josse & Husson, 2012b).”
• Imputation with the R Package VIM (2016, methodology): “…wide range of single imputation algorithms are available, e.g., in the R ( R Core Team 2016) packages yaImpute (Crookston and Finley 2008), missMDA (Josse and Husson 2016), CoImp (Lascio and Giannerini 2016), as well as multiple imputation methods, e.g., in the R packages mice (van Buuren and…”