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

• Published in: Journal of statistical software Vol. 70; no. 1; pp. 1 - 31
• Main Authors: Josse, Julie, Husson, François
• Format: Journal Article
• Language: English
• Published: University of California, Los Angeles 2016; Foundation for Open Access Statistics
• Subjects: Mathematics; missing values; mixed data; multi-table data; multiple correspondence analysis; multiple factor analysis; multiple imputation; principal component analysis; single imputation; Statistics; mixed data; multiple correspondence analysis; multi-table data; multiple imputation; single imputation; principal component analysis; missing values; multiple factor analysis
• ISSN: 1548-7660; 1548-7660
• DOI: 10.18637/jss.v070.i01

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.