epPCA | R Documentation |
Principal Component Analysis (PCA) via ExPosition.
epPCA(DATA, scale = TRUE, center = TRUE, DESIGN = NULL,
make_design_nominal = TRUE, 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. |
graphs |
a boolean. If TRUE (default), graphs and plots are provided
(via |
k |
number of components to return. |
epPCA
performs principal components analysis on a data matrix.
See corePCA
for details on what is returned.
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.
corePCA
, epMDS
data(words)
pca.words.res <- epPCA(words$data)
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