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missMDA (version 1.9)

Handling Missing Values with Multivariate Data Analysis

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.

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Version

Install

install.packages('missMDA')

Monthly Downloads

21,614

Version

1.9

License

GPL (>= 2)

Homepage

http://www.agrocampus-ouest.fr/math/husson

Maintainer

Francois Husson Julie Josse josseagrocampusouestfr

Last Published

December 15th, 2015

Functions in missMDA (1.9)

Impute categorical dataset

Multiple Imputation with PCA

Estimate the number of dimensions for the Factorial Analysis of Mixed Data by cross-validation

Estimate the number of dimensions for the Principal Component Analysis by cross-validation

Characterization of people who snore

Gene expression

Impute dataset with PCA

missMDA-package

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

Impute mixed dataset

Converts a dataset imputed by MIMCA or MIPCA into a mids object

Overimputation diagnostic plot

Questionnaire done by 1232 individuals who answered 14 questions

Multiple Imputation with MCA

Categorical data set with missing values: Survival of passengers on the Titanic

Sensory description of 12 orange juices by 8 attributes.

Genotype-environment data set with missing values

Estimate the number of dimensions for the Multiple Correspondence Analysis by cross-validation

Daily measurements of meteorological variables and ozone concentration

Impute dataset with variables structured into groups of variables (groups of continuous or categorical variables)

Plot the graphs for the Multiple Imputation in PCA