missMDA-package: Handling missing values with/in multivariate data analysis...

In missMDA: Handling Missing Values with Multivariate Data Analysis

Handling missing values with/in multivariate data analysis (principal component methods)

Description

handle missing values in exploratory multivariate analysis such as principal component analysis (PCA), multiple correspondence analysis (MCA), factor analysis for mixed data (FAMD) and multiple factor analysis (MFA)
impute missing values in continuous data sets using the PCA model, categorical data sets using MCA, mixed data using FAMD
generate multiple imputed data sets for continuous data using the PCA model and for categorical data using MCA
visualize multiple imputation in PCA and MCA

Details

The package missMDA impute incomplete datasets for quantitative and / or categorical variables

Author(s)

Francois Husson, Julie Josse

Maintainer: [e-mail]

References

Josse, J. & Husson, F. (2012). Handling missing values in exploratory multivariate data analysis methods. Journal de la SFdS, 153(2), pp. 79-99.

Julie Josse, Francois Husson (2016). missMDA: A Package for Handling Missing Values in Multivariate Data Analysis. Journal of Statistical Software, 70(1), 1-31. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v070.i01")}

Audigier, V., Husson, F., and Josse, J. (2016). Multiple imputation for continuous variables using a bayesian principal component analysis. Journal of Statistical Computation and Simulation, 86(11):2140-2156.

Audigier, V., Husson, F., and Josse, J. (2016). A principal component method to impute missing values for mixed data. Advances in Data Analysis and Classification, 10(1):5-26.

Audigier, V., Husson, F., and Josse, J. (2017). Mimca: multiple imputation for categorical variables with multiple correspondence analysis. Statistics and Computing, 27(2):501-518.

Some videos: https://www.youtube.com/playlist?list=PLnZgp6epRBbQzxFnQrcxg09kRt-PA66T_

missMDA documentation built on Nov. 17, 2023, 5:07 p.m.