Learn R Programming

⚠️There's a newer version (2.1) of this package.Take me there.

GDAtools (version 1.0)

A toolbox for the analysis of categorical data in social sciences, and especially Geometric Data Analysis.

Description

This package contains functions for 'specific' MCA (Multiple Correspondence Analysis), 'class specific' MCA, computing and plotting structuring factors and concentration ellipses, 'standardized' MCA, inductive tests and others tools for Geometric Data Analysis. It also provides functions for the translation of logit models coefficients into percentages (forthcoming), weighted contingency tables and an association measure - i.e. Percentages of Maximum Deviation from Independence (PEM).

Copy Link

Version

Install

install.packages('GDAtools')

Monthly Downloads

954

Version

1.0

License

GPL (>= 2)

Maintainer

Nicolas Robette

Last Published

March 4th, 2014

Functions in GDAtools (1.0)

dichotom

Dichotomizes the variables in a data frame
Music

Music (data)
dimdesc.MCA

Describes the dimensions of MCA and variants of MCA
dimcontrib

Describes the contributions to axes for MCA and variants of MCA
speMCA

Performs a 'specific' MCA
plot.multiMCA

Plots Multiple Factor Analysis
modif.rate

Computes the modified rates of variance of a correspondence analysis
csMCA

Performs a 'class specific' MCA
contrib

Computes contributions for a correspondence analysis
plot.speMCA

Plots 'specific' MCA results
getindexcat

Returns the names of the categories in a data frame
conc.ellipse

Adds concentration ellipses to a correspondence analysis graph.
medoids

Computes the medoids of clusters
textindsup

Adds supplementary individuals to a MCA graph
homog.test

Computes a homogeneity test for a categorical supplementary variable
plot.csMCA

Plots 'class specific' MCA results
textvarsup

Adds a categorical supplementary variable to a MCA graph
stMCA

Performs a 'standardized' MCA
multiMCA

Performs Multiple Factor Analysis
dimeta2

Describes the eta2 of a list of supplementary variables for the axes of MCA and variants of MCA
plot.stMCA

Plots 'standardized' MCA results
pem

Computes the local and global Percentages of Maximum Deviation from Independance (PEM)
burt

Computes a Burt table
indsup

Computes statistics for supplementary individuals
varsup

Computes statistics for a categorical supplementary variable
Taste

Taste (data)