ExPosition: coreCA – R documentation

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coreCA

Description

coreCA performs the core of correspondence analysis (CA), multiple correspondence analysis (MCA) and related techniques.

Usage

coreCA(DATA, masses = NULL, weights = NULL, hellinger = FALSE, symmetric = TRUE, 
decomp.approach = 'svd', k = 0)

Arguments

`DATA`	original data to decompose and analyze via the singular value decomposition.
`masses`	a vector or diagonal matrix with masses for the rows (observations). If NULL, one is created or the plain SVD is used.
`weights`	a vector or diagonal matrix with weights for the columns (measures). If NULL, one is created or the plain SVD is used.
`hellinger`	a boolean. If FALSE (default), Chi-square distance will be used. If TRUE, Hellinger distance will be used.
`symmetric`	a boolean. If TRUE (default) symmetric factor scores for rows and columns are computed. If FALSE, the simplex (column-based) will be returned.
`decomp.approach`	string. A switch for different decompositions (typically for speed). See `pickSVD`.
`k`	number of components to return (this is not a rotation, just an a priori selection of how much data should be returned).

Details

This function should not be used directly. Please use epCA or epMCA unless you plan on writing extensions to ExPosition. Any extensions wherein CA is the primary analysis should use coreCA.

Value

Returns a large list of items which are also returned in epCA and epMCA (the help files for those functions will refer to this as well).
All items with a letter followed by an i are for the I rows of a DATA matrix. All items with a letter followed by an j are for the J rows of a DATA matrix.

`fi`	factor scores for the row items.
`di`	square distances of the row items.
`ci`	contributions (to the variance) of the row items.
`ri`	cosines of the row items.
`fj`	factor scores for the column items.
`dj`	square distances of the column items.
`cj`	contributions (to the variance) of the column items.
`rj`	cosines of the column items.
`t`	the percent of explained variance per component (tau).
`eigs`	the eigenvalues from the decomposition.
`pdq`	the set of left singular vectors (pdq$p) for the rows, singular values (pdq$Dv and pdq$Dd), and the set of right singular vectors (pdq$q) for the columns.
`M`	a column-vector or diagonal matrix of masses (for the rows)
`W`	a column-vector or diagonal matrix of weights (for the columns)
`c`	a centering vector (for the columns).
`X`	the final matrix that was decomposed (includes scaling, centering, masses, etc...).
`hellinger`	a boolean. TRUE if Hellinger distance was used.
`symmetric`	a boolean. FALSE if asymmetric factor scores should be computed.

Author(s)

Derek Beaton and Hervé Abdi.

References

Abdi, H., and Williams, L.J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433-459.
Abdi, H., and Williams, L.J. (2010). Correspondence analysis. In N.J. Salkind, D.M., Dougherty, & B. Frey (Eds.): Encyclopedia of Research Design. Thousand Oaks (CA): Sage. pp. 267-278.
Abdi, H. (2007). Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD). In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics.Thousand Oaks (CA): Sage. pp. 907-912. Greenacre, M. J. (2007). Correspondence Analysis in Practice. Chapman and Hall.