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missMDA (version 1.10)

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

1.10

License

GPL (>= 2)

Homepage

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

Maintainer

Francois Husson Julie Josse josseagrocampusouestfr

Last Published

March 25th, 2016

Functions in missMDA (1.10)

Sensory description of 12 orange juices by 8 attributes.

Overimputation diagnostic plot

Plot the graphs for the Multiple Imputation in PCA

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

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

Genotype-environment data set with missing values

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

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

Impute dataset with PCA

Multiple Imputation with PCA

missMDA-package

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

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

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

Gene expression

Daily measurements of meteorological variables and ozone concentration

Characterization of people who snore

Impute categorical dataset

Impute mixed dataset

Questionnaire done by 1232 individuals who answered 14 questions

Multiple Imputation with MCA