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