Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Julie Josse, François Husson
Title: missMDA: A Package for Handling Missing Values in Multivariate Data Analysis
Abstract: 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.

Page views:: 11972. Submitted: 2014-09-10. Published: 2016-04-04.
Paper: missMDA: A Package for Handling Missing Values in Multivariate Data Analysis     Download PDF (Downloads: 12241)
missMDA_1.10.tar.gz: R source package Download (Downloads: 540; 188KB)
v70i01.R: R replication code Download (Downloads: 536; 2KB)

DOI: 10.18637/jss.v070.i01

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.