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missMDA (version 1.6)

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

1.6

License

GPL (>= 2)

Maintainer

Francois Husson Julie Josse josseagrocampusouestfr

Last Published

February 12th, 2013

Functions in missMDA (1.6)

imputeFAMD

Impute dataset with mixed data
MIPCA

Multiple Imputation with PCA
estim_ncpPCA

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

Impute dataset with PCA
ozone

Daily measurements of meteorological variables and ozone concentration
vnf

Questionnaire done by 1232 individuals who answered 14 questions
orange

Sensory description of 12 orange juices by 8 attributes.
estim_ncpMCA

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

Impute missing values in categorical variables with Multiple Correspondence Analysis
plot.MIPCA

Plot the graphs for the Multiple Imputation in PCA
imputeMFA

Impute dataset with MFA