d_PCA: Principal Component Analysis

In egenn/rtemis: Machine Learning and Visualization

Principal Component Analysis

Description

Perform PCA decomposition using stats::prcomp

Usage

d_PCA(
  x,
  x.test = NULL,
  k = NULL,
  scale = TRUE,
  center = TRUE,
  verbose = TRUE,
  ...
)

Arguments

x	Input matrix
x.test	Optional test set. Will be projected on to PCA basis
k	Integer: Number of right singular vectors to compute (svd's nv)
scale	Logical: If TRUE, scale input data before doing SVD
center	Logical: If TRUE, also center input data if scale is TRUE
verbose	Logical: If TRUE, print messages to screen. Default = TRUE
...	Additional parameters to be passed to PCA::PCA

Details

Same solution as d_SVD. d_PCA runs prcomp, which has useful summary output

Value

rtDecom object

Author(s)

E.D. Gennatas

See Also

Other Decomposition: d_H2OAE(), d_H2OGLRM(), d_ICA(), d_Isomap(), d_KPCA(), d_LLE(), d_MDS(), d_NMF(), d_SPCA(), d_SVD(), d_TSNE(), d_UMAP()

egenn/rtemis documentation built on Dec. 17, 2024, 6:16 p.m.