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ExPosition (version 2.11.0)

Exploratory Analysis with the Singular Value Decomposition

Description

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) .

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Version

Install

install.packages('ExPosition')

Monthly Downloads

1,772

Version

2.11.0

License

GPL-2

Maintainer

Derek Beaton

Last Published

April 13th, 2025

Functions in ExPosition (2.11.0)

Correspondence analysis preprocessing

caSupplementalElementsPreProcessing

Correspondence Analysis preprocessing.

epMCA: Multiple Correspondence Analysis (MCA) via ExPosition.

epMDS: Multidimensional Scaling (MDS) via ExPosition.

epPCA: Principal Component Analysis (PCA) via ExPosition.

Scaling functions for ExPosition.

Small data set on flavor perception and preferences for coffee.

makeRowProfiles

Preprocessing for CA-based analyses

acknowledgements

acknowledgements

Twelve wines from 3 regions in France with 16 attributes.

createDefaultDesign

createDefaultDesign

Fisher's iris Set (for ExPosition)

Faces analyzed using Four Algorithms

epCA: Correspondence Analysis (CA) via ExPosition.

Alzheimer's Patient-Spouse Dyads.

Data from 17 Alzheimer's Patient-Spouse dyads.

Data of categories of images as view in an fMRI experiment.

genPDQ: the GSVD

great.beer.tasting.1

A collection of beer tasting notes from untrained assessors.

Six wines described by several assessors with rank attributes.

hellingerSupplementaryColsPreProcessing

Preprocessing for supplementary columns in Hellinger analyses.

Pick which generalized SVD (or related) decomposition to use.

Print Correspondence Analysis (CA) results

great.beer.tasting.2

A collection of beer tasting notes from untrained assessors.

epGraphs: ExPosition plotting function

Hellinger version of CA preprocessing

Print Multidimensional Scaling (MDS) results

Checks if data are disjunctive.

Transform data for MDS analysis.

makeDistancesAndWeights

Makes distances and weights for MDS analyses (see epMDS).

makeNominalData

makeNominalData

hellingerSupplementaryRowsPreProcessing

Preprocessing for supplementary rows in Hellinger analyses.

Print results from the singular value decomposition (SVD) in ExPosition

How twelve French families spend their income on groceries.

Normalize the rows of a matrix.

Perform Rv coefficient computation.

supplementalProjection

Supplemental projections.

Six wines described by several assessors with qualitative attributes.

print.expoOutput

Print results from ExPosition

mdsSupplementalElementsPreProcessing

MDS preprocessing

Print epGraphs results

pcaSupplementaryColsPreProcessing

Preprocessing for supplementary columns in PCA.

supplementaryRows

Supplementary rows

Print Multiple Correspondence Analysis (MCA) results

pcaSupplementaryRowsPreProcessing

Preprocessing for supplemental rows in PCA.

Print Principal Components Analysis (PCA) results

supplementaryCols

Supplementary columns

Wines Data from 12 assessors described by 15 flavor profiles.

Twenty words described by 2 features.

Small data set for Partial Least Squares-Correspondence Analysis

Six wines described by 3 assessors.

ExPosition-package

ExPosition: Exploratory Analysis with the Singular Value DecomPosition

calculateConstraints

calculateConstraints

beer.tasting.notes

Some of authors' personal beer tasting notes.

(A truncated form of) Punctuation used by six authors (data).

Twelve wines from 3 regions in France with 18 attributes.

Ten assessors sort eight beers into groups.

Chi-square Distance computation