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missMDA (version 1.7.1)

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

7,503

Version

1.7.1

License

GPL (>= 2)

Maintainer

Francois Husson Julie Josse josseagrocampusouestfr

Last Published

May 23rd, 2013

Functions in missMDA (1.7.1)

imputeMFA

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

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

Impute categorical dataset
MIPCA

Multiple Imputation with PCA
imputePCA

Impute dataset with PCA
imputeFAMD

Impute mixed dataset
orange

Sensory description of 12 orange juices by 8 attributes.
estim_ncpPCA

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

Daily measurements of meteorological variables and ozone concentration
plot.MIPCA

Plot the graphs for the Multiple Imputation in PCA
vnf

Questionnaire done by 1232 individuals who answered 14 questions
snorena

Characterization of people who snore
geno

Genotype-environment data set with missing values
gene

Gene expression
missMDA-package

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