ExPosition

ExPosition: Exploratory Analysis with the Singular Value DecomPosition

acknowledgements

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

Twelve wines from 3 regions in France with 18 attributes.

Some of authors' personal beer tasting notes.

Ten assessors sort eight beers into groups.

Correspondence analysis preprocessing

Correspondence Analysis preprocessing.

calculateConstraints

Chi-square Distance computation

Small data set on flavor perception and preferences for coffee.

computeMW

coreCA

coreMDS

corePCA

createDefaultDesign

designCheck

Alzheimer's Patient-Spouse Dyads.

Twelve wines from 3 regions in France with 16 attributes.

Fisher's iris Set (for ExPosition)

epCA: Correspondence Analysis (CA) via ExPosition.

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

epGraphs: ExPosition plotting function

epMCA: Multiple Correspondence Analysis (MCA) via ExPosition.

epMDS: Multidimensional Scaling (MDS) via ExPosition.

epPCA: Principal Component Analysis (PCA) via ExPosition.

Scaling functions for ExPosition.

Faces analyzed using Four Algorithms

How twelve French families spend their income on groceries.

genPDQ: the GSVD

A collection of beer tasting notes from untrained assessors.

A collection of beer tasting notes from untrained assessors.

Hellinger version of CA preprocessing

Preprocessing for supplementary columns in Hellinger analyses.

Preprocessing for supplementary rows in Hellinger analyses.

Data from 17 Alzheimer's Patient-Spouse dyads.

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

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

makeNominalData

Preprocessing for CA-based analyses

mca.eigen.fix

Six wines described by several assessors with qualitative attributes.

MDS preprocessing

Transform data for MDS analysis.

Checks if data are disjunctive.

pause

Six wines described by several assessors with rank attributes.

Preprocessing for supplementary columns in PCA.

Preprocessing for supplemental rows in PCA.

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

Print Correspondence Analysis (CA) results

Print Generalized Principal Components Analysis (GPCA) results

Print epGraphs results

Print Multiple Correspondence Analysis (MCA) results

Print Multidimensional Scaling (MDS) results

Print Principal Components Analysis (PCA) results

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

Print results from ExPosition

Normalize the rows of a matrix.

Perform Rv coefficient computation.

Small data set for Partial Least Squares-Correspondence Analysis

Supplemental projections.

Supplementary columns

Supplementary rows

Six wines described by 3 assessors.

Wines Data from 12 assessors described by 15 flavor profiles.

Twenty words described by 2 features.