文章基本信息

标题：missMDA: A Package for Handling Missing Values in Multivariate Data Analysis
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作者：Julie Josse ; François Husson
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2016
卷号：70
期号：1
页码：1-31
语种：English
出版社：University of California, Los Angeles
摘要：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.
关键词：missing values;principal component analysis;single imputation;multiple imputation;multi-table data;mixed data;multiple correspondence analysis;multiple factor analysis