Man pages for ExPosition
Exploratory Analysis with the Singular Value Decomposition
| acknowledgements | acknowledgements |
| authors | (A truncated form of) Punctuation used by six authors (data). |
| bada.wine | Twelve wines from 3 regions in France with 18 attributes. |
| beers2007 | Ten assessors sort eight beers into groups. |
| beer.tasting.notes | Some of authors' personal beer tasting notes. |
| calculateConstraints | calculateConstraints |
| caNorm | Correspondence analysis preprocessing |
| caSupplementalElementsPreProcessing | Correspondence Analysis preprocessing. |
| chi2Dist | Chi-square Distance computation |
| coffee.data | Small data set on flavor perception and preferences for... |
| computeMW | computeMW |
| coreCA | coreCA |
| coreMDS | coreMDS |
| corePCA | corePCA |
| createDefaultDesign | createDefaultDesign |
| designCheck | designCheck |
| dica.ad | Alzheimer's Patient-Spouse Dyads. |
| dica.wine | Twelve wines from 3 regions in France with 16 attributes. |
| epCA | epCA: Correspondence Analysis (CA) via ExPosition. |
| epGPCA | epGPCA: Generalized Principal Components Analysis (GPCA) via... |
| epGraphs | epGraphs: ExPosition plotting function |
| ep.iris | Fisher's iris Set (for ExPosition) |
| epMCA | epMCA: Multiple Correspondence Analysis (MCA) via ExPosition. |
| epMDS | epMDS: Multidimensional Scaling (MDS) via ExPosition. |
| epPCA | epPCA: Principal Component Analysis (PCA) via ExPosition. |
| expo.scale | Scaling functions for ExPosition. |
| ExPosition-package | ExPosition: _Ex_ploratory Analysis with the Singular Value... |
| faces2005 | Faces analyzed using Four Algorithms |
| french.social | How twelve French families spend their income on groceries. |
| genPDQ | genPDQ: the GSVD |
| 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. |
| hellingerNorm | Hellinger version of CA preprocessing |
| hellingerSupplementaryColsPreProcessing | Preprocessing for supplementary columns in Hellinger... |
| hellingerSupplementaryRowsPreProcessing | Preprocessing for supplementary rows in Hellinger analyses. |
| 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. |
| makeDistancesAndWeights | Makes distances and weights for MDS analyses (see 'epMDS'). |
| makeNominalData | makeNominalData |
| makeRowProfiles | Preprocessing for CA-based analyses |
| mca.eigen.fix | mca.eigen.fix |
| mca.wine | Six wines described by several assessors with qualitative... |
| mdsSupplementalElementsPreProcessing | MDS preprocessing |
| mdsTransform | Transform data for MDS analysis. |
| nominalCheck | Checks if data are disjunctive. |
| pause | pause |
| pcaSupplementaryColsPreProcessing | Preprocessing for supplementary columns in PCA. |
| pcaSupplementaryRowsPreProcessing | Preprocessing for supplemental rows in PCA. |
| pca.wine | Six wines described by several assessors with rank... |
| pickSVD | Pick which generalized SVD (or related) decomposition to use. |
| print.epCA | Print Correspondence Analysis (CA) results |
| print.epGPCA | Print Generalized Principal Components Analysis (GPCA)... |
| print.epGraphs | Print epGraphs results |
| print.epMCA | Print Multiple Correspondence Analysis (MCA) results |
| print.epMDS | Print Multidimensional Scaling (MDS) results |
| print.epPCA | Print Principal Components Analysis (PCA) results |
| print.epSVD | Print results from the singular value decomposition (SVD) in... |
| print.expoOutput | Print results from ExPosition |
| rowNorms | Normalize the rows of a matrix. |
| rvCoeff | Perform Rv coefficient computation. |
| snps.druguse | Small data set for Partial Least Squares-Correspondence... |
| supplementalProjection | Supplemental projections. |
| supplementaryCols | Supplementary columns |
| supplementaryRows | Supplementary rows |
| 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. |
