Package: ExPosition 2.11.0

Derek Beaton

ExPosition: Exploratory Analysis with the Singular Value Decomposition

A variety of descriptive multivariate analyses with the singular value decomposition, such as principal components analysis, correspondence analysis, and multidimensional scaling. See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.

Authors:Derek Beaton [aut, cre], Cherise R. Chin Fatt [aut], Herve Abdi [aut]

ExPosition_2.11.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ExPosition/json (API)

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

Bug tracker:https://github.com/derekbeaton/exposition1/issues

Datasets:
  • authors - (A truncated form of) Punctuation used by six authors (data).
  • bada.wine - Twelve wines from 3 regions in France with 18 attributes.
  • beer.tasting.notes - Some of authors' personal beer tasting notes.
  • beers2007 - Ten assessors sort eight beers into groups.
  • coffee.data - Small data set on flavor perception and preferences for coffee.
  • dica.ad - Alzheimer's Patient-Spouse Dyads.
  • dica.wine - Twelve wines from 3 regions in France with 16 attributes.
  • ep.iris - Fisher's iris Set
  • faces2005 - Faces analyzed using Four Algorithms
  • french.social - How twelve French families spend their income on groceries.
  • great.beer.tasting.1 - A collection of beer tasting notes from untrained assessors.
  • great.beer.tasting.2 - A collection of beer tasting notes from untrained assessors.
  • jlsr.2010.ad - Data from 17 Alzheimer's Patient-Spouse dyads.
  • jocn.2005.fmri - Data of categories of images as view in an _f_MRI experiment.
  • mca.wine - Six wines described by several assessors with qualitative attributes.
  • pca.wine - Six wines described by several assessors with rank attributes.
  • snps.druguse - Small data set for Partial Least Squares-Correspondence Analysis
  • wines2007 - Six wines described by 3 assessors.
  • wines2012 - Wines Data from 12 assessors described by 15 flavor profiles.
  • words - Twenty words described by 2 features.

On CRAN:

Conda:

6.37 score 5 stars 7 packages 168 scripts 1.3k downloads 5 mentions 42 exports 1 dependencies

Last updated from:3c39addecb. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK156
source / vignettesOK138
linux-release-x86_64OK113
macos-release-arm64OK117
macos-oldrel-arm64OK85
windows-develOK65
windows-releaseOK71
windows-oldrelOK76
wasm-releaseOK103

Exports:acknowledgementscalculateConstraintscaNormcaSupplementalElementsPreProcessingchi2DistcomputeMWcoreCAcoreMDScorePCAcreateDefaultDesigndesignCheckepCAepGraphsepMCAepMDSepPCAexpo.scalegenPDQhellingerNormmakeNominalDatamakeRowProfilesmca.eigen.fixmdsSupplementalElementsPreProcessingmdsTransformnominalCheckpausepcaSupplementaryColsPreProcessingpcaSupplementaryRowsPreProcessingpickSVDprint.epCAprint.epGraphsprint.epMCAprint.epMDSprint.epPCAprint.epSVDprint.expoOutputrowNormsrvCoeffsqrt_matsupplementalProjectionsupplementaryColssupplementaryRows

Dependencies:prettyGraphs

Readme and manuals

Help Manual

Help pageTopics
ExPosition: _Ex_ploratory Analysis with the Singular Value Decom_Position_ExPosition-package ExPosition
acknowledgementsacknowledgements
(A truncated form of) Punctuation used by six authors (data).authors
Twelve wines from 3 regions in France with 18 attributes.bada.wine
Some of authors' personal beer tasting notes.beer.tasting.notes
Ten assessors sort eight beers into groups.beers2007
calculateConstraintscalculateConstraints
Correspondence analysis preprocessingcaNorm
Correspondence Analysis preprocessing.caSupplementalElementsPreProcessing
Chi-square Distance computationchi2Dist
Small data set on flavor perception and preferences for coffee.coffee.data
computeMWcomputeMW
coreCAcoreCA
coreMDScoreMDS
corePCAcorePCA
createDefaultDesigncreateDefaultDesign
designCheckdesignCheck
Alzheimer's Patient-Spouse Dyads.dica.ad
Twelve wines from 3 regions in France with 16 attributes.dica.wine
Fisher's iris Set (for ExPosition)ep.iris
epCA: Correspondence Analysis (CA) via ExPosition.epCA
epGraphs: ExPosition plotting functionepGraphs
epMCA: Multiple Correspondence Analysis (MCA) via ExPosition.epMCA
epMDS: Multidimensional Scaling (MDS) via ExPosition.epMDS
epPCA: Principal Component Analysis (PCA) via ExPosition.epPCA
Scaling functions for ExPosition.expo.scale
Faces analyzed using Four Algorithmsfaces2005
How twelve French families spend their income on groceries.french.social
genPDQ: the GSVDgenPDQ
A collection of beer tasting notes from untrained assessors.great.beer.tasting.1
A collection of beer tasting notes from untrained assessors.great.beer.tasting.2
Hellinger version of CA preprocessinghellingerNorm
Preprocessing for supplementary columns in Hellinger analyses.hellingerSupplementaryColsPreProcessing
Preprocessing for supplementary rows in Hellinger analyses.hellingerSupplementaryRowsPreProcessing
Data from 17 Alzheimer's Patient-Spouse dyads.jlsr.2010.ad
Data of categories of images as view in an _f_MRI experiment.jocn.2005.fmri
Makes distances and weights for MDS analyses (see 'epMDS').makeDistancesAndWeights
makeNominalDatamakeNominalData
Preprocessing for CA-based analysesmakeRowProfiles
mca.eigen.fixmca.eigen.fix
Six wines described by several assessors with qualitative attributes.mca.wine
MDS preprocessingmdsSupplementalElementsPreProcessing
Transform data for MDS analysis.mdsTransform
Checks if data are disjunctive.nominalCheck
pausepause
Six wines described by several assessors with rank attributes.pca.wine
Preprocessing for supplementary columns in PCA.pcaSupplementaryColsPreProcessing
Preprocessing for supplemental rows in PCA.pcaSupplementaryRowsPreProcessing
Pick which generalized SVD (or related) decomposition to use.pickSVD
Print Correspondence Analysis (CA) resultsprint.epCA
Print epGraphs resultsprint.epGraphs
Print Multiple Correspondence Analysis (MCA) resultsprint.epMCA
Print Multidimensional Scaling (MDS) resultsprint.epMDS
Print Principal Components Analysis (PCA) resultsprint.epPCA
Print results from the singular value decomposition (SVD) in ExPositionprint.epSVD
Print results from ExPositionprint.expoOutput
Normalize the rows of a matrix.rowNorms
Perform Rv coefficient computation.rvCoeff
Small data set for Partial Least Squares-Correspondence Analysissnps.druguse
sqrt_matsqrt_mat
Supplemental projections.supplementalProjection
Supplementary columnssupplementaryCols
Supplementary rowssupplementaryRows
Six wines described by 3 assessors.wines2007
Wines Data from 12 assessors described by 15 flavor profiles.wines2012
Twenty words described by 2 features.words