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missMDA (version 1.7.3)

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

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

1.7.3

License

GPL (>= 2)

Homepage

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

Maintainer

Francois Husson Julie Josse josseagrocampusouestfr

Last Published

November 24th, 2014

Functions in missMDA (1.7.3)

Gene expression

Plot the graphs for the Multiple Imputation in PCA

Daily measurements of meteorological variables and ozone concentration

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

Multiple Imputation with PCA

Characterization of people who snore

Questionnaire done by 1232 individuals who answered 14 questions

Impute mixed dataset

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

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

missMDA-package

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

Impute dataset with PCA

Genotype-environment data set with missing values

Sensory description of 12 orange juices by 8 attributes.

Impute categorical dataset