Package: cabootcrs 2.1.0

Trevor Ringrose

cabootcrs: Bootstrap Confidence Regions for Simple and Multiple Correspondence Analysis

Performs simple correspondence analysis on a two-way contingency table, or multiple correspondence analysis (homogeneity analysis) on data with p categorical variables, and produces bootstrap-based elliptical confidence regions around the projected coordinates for the category points. Includes routines to plot the results in a variety of styles. Also reports the standard numerical output for correspondence analysis.

Authors:Trevor Ringrose

cabootcrs_2.1.0.tar.gz
cabootcrs_2.1.0.zip(r-4.7-any)cabootcrs_2.1.0.zip(r-4.6-any)
cabootcrs_2.1.0.tar.gz(r-4.7-any)cabootcrs_2.1.0.tar.gz(r-4.6-any)
cabootcrs_2.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
cabootcrs/json (API)

# Install 'cabootcrs' in R:
install.packages('cabootcrs', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 20 scripts 473 downloads 17 exports 2 dependencies

Last updated from:a5e3efc3f1. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK107
source / vignettesOK209
linux-releaseOK103
windows-develOK100
windows-releaseOK82
wasm-releaseOK96

Exports:addsupplementaryallvarscovscabootcrsconvertcovmatgetBurtgetCTgetdoubledgetindicatormyresamplefnplotcaprintcarearrangereflectaxesreordercategoriesscasummaryca

Dependencies:colorspacelpSolve

Readme and manuals

Help Manual

Help pageTopics
Bootstrap Confidence Regions for Simple and Multiple Correspondence Analysiscabootcrs-package
Calculate coordinates for supplementary points, with option to add to the currently selected plot.addsupplementary
Extract all variances and covariances in readable form as a data frameallvarscovs
Asbestos dataAsbestosData
van Ijzendoorn's attachment dataAttachmentData
A class containing the basic results from CAcabasicresults-class
Calculate category point variances using bootstrappingcabootcrs
A class containing the results from CA with bootstrappingcabootcrsresults-class
Converting a data matrix from one format into anotherconvert
Extract a single 2 by 2 covariance matrixcovmat
Maxwell's dream data set, with simplified labelsDreamData
Maxwell's dream data set with added totally random columnDreamData223by3
Maxwell's dream data set, using full original labelsDreamDataNames
Converting a data matrix into a Burt matrixgetBurt
Converting a data matrix into a contingency tablegetCT
Converting a data matrix into a doubled matrixgetdoubled
Converting a data matrix into an indicator matrixgetindicator
Example of a user-generated resampling routine.myresamplefn
Nishisato's Singapore dataNishData
Osteoarchaeological data with categories given as numbersOsteoData
Osteoarchaeological data with named categoriesOsteoDataNames
Plotting results with confidence regionsplotca
Prints reasonably full results, including variancesprintca
Rearranges bootstrap axes by comparing to sample axesrearrange
Old and rubbish algorithm to rearrange bootstrap axes by comparing to sample axesrearrange_old
Reflect coordinates for chosen axesreflectaxes
Reorder categories for chosen variable in MCA case onlyreordercategories
Performs standard Correspondence Analysis calculationssca
Internal function to be used by printca and summarycasettingsinertias
Suicide dataSuicideData
Prints brief 2-d results, with standard deviationssummaryca