References on missing values
How to cite the package missMDA?
- Josse J. & Husson F. (2016). missMDA: a package for handling missing values in multivariate data analysis. Journal of Statistical Software, 70(1), 1-31.
Vignette:
- Multiple imputation with principal component methods: a user guide written by V. Audigier [pdf]
Papers:
- Audigier V., Husson F. & Josse J. (2017). MIMCA: multiple imputation for categorical variables with multiple correspondence analysis. Statistics and Computing, 27(2):501-518.
- Josse J. & Husson F. (2016). missMDA: a package for handling missing values in multivariate data analysis. Journal of Statistical Software, 70(1), 1-31.
- Audigier V, Husson F & 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 & Josse J. (2016). A principal component method to impute missing values for mixed data. Advances in Data Analysis and Classification, 10(1):5-26.
- Josse J & Husson F. (2013). Handling missing values in exploratory multivariate data analysis methods. Journal de la SFDS. 153 (2), pp. 79-99.
- Josse J, Chavent M., Liquet B. & Husson F.(2012). Handling missing values with Regularized Iterative Multiple Correspondence Analysis. Journal of classification. 29 (1), pp.91-116.
- Josse J & Husson F. (2011). Selecting the number of components in PCA using cross-validation approximations.Computational Statististics and Data Analysis. 56 (6), pp. 1869-1879.
- Josse J, Husson H. & Pagès J. (2011). Multiple imputation in PCA. Advances in data analysis and classification. 5 (3), pp. 231-246.
Tutorials:
Tutorial done during the useR! 2015 conference (Aalborg, DK)
The tutorial Handling missing values with a special focus on the use of principal components methods done in Aalborg during the useR! 2015 conference.
The following packages are used: FactoMineR, missMDA, VIM, Amelia, norm, mice.
The data sets and the lines of code are available to redo the analyses done in the tutorial.
- Slides of the presentation
- Ozone data: dataset, lines of code
- Ecological data: dataset, lines of code
- useR!2016, Stanford
- Gdr Ecology stat, Lyon 2016
- INRIA, saclay Data Science Center, 2016
Other conferences:
- Missing values imputation for mixed data based on principal component methods. COMPSTAT, Cyprus, 27-31th 2012. slides.
- Imputation de données manquantes pour des données mixtes via les méthodes factorielles grâce à missMDA. Premières rencontres R, Bordeaux. July 2-3th 2012. abstract - slides.
- missMDA : a package to handle missing values in and with multivariate exploratory data analysis methods. useR ! 2011, Warwick, England, August 15-20th. (abstract, slides).