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missMDA (version 1.13)

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 or MCA.

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Version

Install

install.packages('missMDA')

Monthly Downloads

6,619

Version

1.13

License

GPL (>= 2)

Last Published

June 25th, 2018

Functions in missMDA (1.13)

plot.MIPCA

Plot the graphs for the Multiple Imputation in PCA
imputeMultilevel

Impute a multilevel mixed dataset
prelim

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

Questionnaire done by 1232 individuals who answered 14 questions
snorena

Characterization of people who snore
Overimpute

Overimputation diagnostic plot
TitanicNA

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

Impute contingency table
estim_ncpFAMD

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

Multiple Imputation with MCA
MIPCA

Multiple Imputation with PCA
estim_ncpPCA

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

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

Gene expression
imputeMFA

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

Genotype-environment data set with missing values
missMDA-package

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

Impute mixed dataset
orange

Sensory description of 12 orange juices by 8 attributes.
imputePCA

Impute dataset with PCA
plot.MIMCA

Plot the graphs for the Multiple Imputation in MCA
ozone

Daily measurements of meteorological variables and ozone concentration
imputeMCA

Impute categorical dataset