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

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...