Exploratory Analysis with the Singular Value Decomposition
Supplementary columns
Supplementary rows
acknowledgements
calculateConstraints
Correspondence analysis preprocessing
Correspondence Analysis preprocessing.
Chi-square Distance computation
computeMW
coreCA
coreMDS
corePCA
createDefaultDesign
designCheck
epCA: Correspondence Analysis (CA) via ExPosition.
epGPCA: Generalized Principal Components Analysis (GPCA) via ExPositio...
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.
ExPosition: Exploratory Analysis with the Singular Value Decom**Po...
genPDQ: the GSVD
Hellinger version of CA preprocessing
Preprocessing for supplementary columns in Hellinger analyses.
Preprocessing for supplementary rows in Hellinger analyses.
Makes distances and weights for MDS analyses (see epMDS).
makeNominalData
Preprocessing for CA-based analyses
mca.eigen.fix
MDS preprocessing
Transform data for MDS analysis.
Checks if data are disjunctive.
pause
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 ExPositio...
Print results from ExPosition
Normalize the rows of a matrix.
Perform Rv coefficient computation.
Supplemental projections.
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>.