missMDA: A Package for Handling Missing Values in Multivariate Data 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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2016-04-01
|
| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/2615 |
| _version_ | 1819012290788196352 |
|---|---|
| author | Julie Josse François Husson |
| author_facet | Julie Josse François Husson |
| author_sort | Julie Josse |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T01:41:42Z |
| format | Article |
| id | doaj.art-eacaab5b2f4e44ba9998751ebfba603b |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-21T01:41:42Z |
| publishDate | 2016-04-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-eacaab5b2f4e44ba9998751ebfba603b2022-12-21T19:20:08ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602016-04-0170113110.18637/jss.v070.i01990missMDA: A Package for Handling Missing Values in Multivariate Data AnalysisJulie JosseFrançois HussonWe 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.https://www.jstatsoft.org/index.php/jss/article/view/2615missing valuesprincipal component analysissingle imputationmultiple imputationmulti-table datamixed datamultiple correspondence analysismultiple factor analysis |
| spellingShingle | Julie Josse François Husson missMDA: A Package for Handling Missing Values in Multivariate Data Analysis Journal of Statistical Software missing values principal component analysis single imputation multiple imputation multi-table data mixed data multiple correspondence analysis multiple factor analysis |
| title | missMDA: A Package for Handling Missing Values in Multivariate Data Analysis |
| title_full | missMDA: A Package for Handling Missing Values in Multivariate Data Analysis |
| title_fullStr | missMDA: A Package for Handling Missing Values in Multivariate Data Analysis |
| title_full_unstemmed | missMDA: A Package for Handling Missing Values in Multivariate Data Analysis |
| title_short | missMDA: A Package for Handling Missing Values in Multivariate Data Analysis |
| title_sort | missmda a package for handling missing values in multivariate data analysis |
| topic | missing values principal component analysis single imputation multiple imputation multi-table data mixed data multiple correspondence analysis multiple factor analysis |
| url | https://www.jstatsoft.org/index.php/jss/article/view/2615 |
| work_keys_str_mv | AT juliejosse missmdaapackageforhandlingmissingvaluesinmultivariatedataanalysis AT francoishusson missmdaapackageforhandlingmissingvaluesinmultivariatedataanalysis |