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)
DATA |
original data to perform a PCA on. |
scale |
a boolean, vector, or string. See |
center |
a boolean, vector, or string. See |
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 |
k |
number of components to return. |
epGPCA performs generalized principal components analysis. Essentially, a PCA with masses and weights for rows and columns, respectively.
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. |
Derek Beaton
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.
#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)
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