epGPCA function

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

Generalized Principal Components Analysis (GPCA) via ExPosition.

epGPCA(DATA, scale = TRUE, center = TRUE, DESIGN = NULL, make_design_nominal = TRUE, 
        masses = NULL, weights = NULL, graphs = TRUE, k = 0)

Arguments

• DATA: original data to perform a PCA on.
• scale: a boolean, vector, or string. See expo.scale for details.
• center: a boolean, vector, or string. See expo.scale for details.
• DESIGN: a design matrix to indicate if rows belong to groups.
• make_design_nominal: a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix.
• masses: a diagonal matrix or column-vector of masses for the row items.
• weights: a diagonal matrix or column-vector of weights for the column items.
• graphs: a boolean. If TRUE (default), graphs and plots are provided (via epGraphs)
• k: number of components to return.

Details

epGPCA performs generalized principal components analysis. Essentially, a PCA with masses and weights for rows and columns, respectively.

Returns

See corePCA for details on what is returned. In addition to the values in corePCA:

• M: a matrix (or vector, depending on size) of masses for the row items.

• W: a matrix (or vector, depending on size) of weights for the column items.

References

Abdi, H., and Williams, L.J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433-459.

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.

Author(s)

Derek Beaton

See Also

corePCA, epPCA, epMDS

Examples

#this is for ExPosition's iris data
        data(ep.iris)
        gpca.iris.res <- epGPCA(ep.iris$data,DESIGN=ep.iris$design,make_design_nominal=FALSE)