Handling Missing Values with Multivariate Data Analysis

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Documentation for package ‘missMDA’ version 1.19

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

missMDA-package	Handling missing values with/in multivariate data analysis (principal component methods)
estim_ncpFAMD	Estimate the number of dimensions for the Factorial Analysis of Mixed Data by cross-validation
estim_ncpMCA	Estimate the number of dimensions for the Multiple Correspondence Analysis by cross-validation
estim_ncpMultilevel	Estimate the number of dimensions for the Multilevel PCA, multlevel MCA or Multilevel FAMD by cross-validation
estim_ncpPCA	Estimate the number of dimensions for the Principal Component Analysis by cross-validation
gene	Gene expression
geno	Genotype-environment data set with missing values
imputeCA	Impute contingency table
imputeFAMD	Impute mixed dataset
imputeMCA	Impute categorical dataset
imputeMFA	Impute dataset with variables structured into groups of variables (groups of continuous or categorical variables)
imputeMultilevel	Impute a multilevel mixed dataset
imputePCA	Impute dataset with PCA
MIFAMD	Multiple Imputation with FAMD
MIMCA	Multiple Imputation with MCA
MIPCA	Multiple Imputation with PCA
missMDA	Handling missing values with/in multivariate data analysis (principal component methods)
orange	Sensory description of 12 orange juices by 8 attributes.
Overimpute	Overimputation diagnostic plot
ozone	Daily measurements of meteorological variables and ozone concentration
plot.MIMCA	Plot the graphs for the Multiple Imputation in MCA
plot.MIPCA	Plot the graphs for the Multiple Imputation in PCA
prelim	Converts a dataset imputed by MIMCA, MIPCA or MIFAMD into a mids object
snorena	Characterization of people who snore
TitanicNA	Categorical data set with missing values: Survival of passengers on the Titanic
vnf	Questionnaire done by 1232 individuals who answered 14 questions