| missMDA-package {missMDA} | R Documentation |
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: francois.husson@institut-agro.fr
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. 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_