Package:
VIM
Authors:
Matthias Templ [aut, cre], Alexander Kowarik [aut], Andreas Alfons [aut], Gregor de Cillia [aut], Bernd Prantner [ctb], Wolfgang Rannetbauer [aut]
Category:
Single Imputation, Visualisations for Missing Data
Use-Cases:
Single imputation for numerical, categorical, ordered and semi-continous variables, Missing data visualizations
Popularity:
Description:
New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.
Read more →Package:
missMDA
Authors:
Francois Husson, Julie Josse
Category:
Single and Multiple Imputation
Use-Cases:
Imputation of incomplete continuous, categorical or mixed datasets, …
Popularity:
Description:
Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.
Last update:
Algorithms:
- Continuous data (multiple) imputation (with Principal Components Analysis)
- Contingency table imputation (with Correspondence Analysis)
- Mixed data (multiple) imputation (with Factorial Analysis of Mixed Data)
- Categorical data (multiple) imputation (with Multiple Correspondence Analysis)
- Structured data imputation (with Multiple Factor Analysis)
- Multilevel mixed data imputation (with Multilevel Factorial Analysis for Mixed Data)
- Overimputation diagnostic
Datasets:
- gene
- geno
- orange
- ozone
- snorena
- TitanicNA
- vnf
- Josse, J. & Husson, F. (2012). Handling missing values in exploratory multivariate data analysis methods. Journal de la SFdS, 153(2), pp. 79-99. PDF (on HAL)
- 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. PDF (on HAL)
- 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. PDF (on arXiv)
- 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. PDF (on arXiv)
- 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. PDF (on arXiv)
- Some videos (on Youtube)
data.frame, mids
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