Learn R Programming

⚠️There's a newer version (2.11.0) of this package.Take me there.

ExPosition (version 2.8.23)

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) .

Copy Link

Version

Install

install.packages('ExPosition')

Monthly Downloads

1,772

Version

2.8.23

License

GPL-2

Maintainer

Derek Beaton

Last Published

January 7th, 2019

Functions in ExPosition (2.8.23)

ExPosition-package

ExPosition: Exploratory Analysis with the Singular Value DecomPosition

Small data set on flavor perception and preferences for coffee.

calculateConstraints

calculateConstraints

Chi-square Distance computation

beer.tasting.notes

Some of authors' personal beer tasting notes.

Ten assessors sort eight beers into groups.

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

Correspondence analysis preprocessing

epCA: Correspondence Analysis (CA) via ExPosition.

epGPCA: Generalized Principal Components Analysis (GPCA) via ExPosition.

createDefaultDesign

createDefaultDesign

caSupplementalElementsPreProcessing

Correspondence Analysis preprocessing.

Twelve wines from 3 regions in France with 18 attributes.

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

epGraphs: ExPosition plotting function

epMCA: Multiple Correspondence Analysis (MCA) via ExPosition.

epMDS: Multidimensional Scaling (MDS) via ExPosition.

makeDistancesAndWeights

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

makeNominalData

makeNominalData

How twelve French families spend their income on groceries.

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

genPDQ: the GSVD

epPCA: Principal Component Analysis (PCA) via ExPosition.

print.expoOutput

Print results from ExPosition

makeRowProfiles

Preprocessing for CA-based analyses

Checks if data are disjunctive.

Small data set for Partial Least Squares-Correspondence Analysis

Six wines described by several assessors with rank attributes.

Twelve wines from 3 regions in France with 16 attributes.

hellingerSupplementaryRowsPreProcessing

Preprocessing for supplementary rows in Hellinger analyses.

supplementalProjection

Supplemental projections.

Fisher's iris Set (for ExPosition)

Data from 17 Alzheimer's Patient-Spouse dyads.

Alzheimer's Patient-Spouse Dyads.

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

pcaSupplementaryRowsPreProcessing

Preprocessing for supplemental rows in PCA.

pcaSupplementaryColsPreProcessing

Preprocessing for supplementary columns in PCA.

Print Correspondence Analysis (CA) results

Hellinger version of CA preprocessing

Print epGraphs results

Normalize the rows of a matrix.

Print Generalized Principal Components Analysis (GPCA) results

hellingerSupplementaryColsPreProcessing

Preprocessing for supplementary columns in Hellinger analyses.

Perform Rv coefficient computation.

Scaling functions for ExPosition.

mdsSupplementalElementsPreProcessing

MDS preprocessing

Print Multiple Correspondence Analysis (MCA) results

supplementaryCols

Supplementary columns

supplementaryRows

Supplementary rows

Transform data for MDS analysis.

Faces analyzed using Four Algorithms

great.beer.tasting.1

A collection of beer tasting notes from untrained assessors.

Twenty words described by 2 features.

great.beer.tasting.2

A collection of beer tasting notes from untrained assessors.

Six wines described by several assessors with qualitative attributes.

Print Multidimensional Scaling (MDS) results

Print Principal Components Analysis (PCA) results

Six wines described by 3 assessors.

Wines Data from 12 assessors described by 15 flavor profiles.

acknowledgements

acknowledgements