aespca {pathwayPCA} | R Documentation |
A function to perform adaptive, elastic-net, sparse principal component analysis (AES-PCA).
aespca(X, d = 1, max.iter = 10, eps.conv = 0.001, adaptive = TRUE, para = NULL)
X |
A pathway design matrix: the data matrix should be n \times p, where n is the sample size and p is the number of variables included in the pathway. |
d |
The number of principal components (PCs) to extract from the pathway. Defaults to 1. |
max.iter |
The maximum number of times an internal |
eps.conv |
A numerical convergence threshold for the same |
adaptive |
Internal argument of the |
para |
Internal argument of the |
This function calculates the loadings and reduced-dimension predictor matrix using both the Singular Value Decomposition and AES-PCA Decomposition (as described in Efron et al (2003)) of the data matrix.
See https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf.
For potential enhancement details, see the comment in the "Details"
section of normalize
.
A list of four elements containing the loadings and projected predictors:
aesLoad
: A d \times p projection matrix of the
d AES-PCs.
oldLoad
: A d \times p projection matrix of the
d PCs from the singular value decomposition (SVD).
aesScore
: An n \times d predictor matrix: the
original n observations loaded onto the d AES-PCs.
oldScore
: An n \times d predictor matrix: the
original n observations loaded onto the d SVD-PCs.
normalize
; lars.lsa
;
ExtractAESPCs
; AESPCA_pVals
# DO NOT CALL THIS FUNCTION DIRECTLY. # Call this function through AESPCA_pVals() instead. ## Not run: data("colonSurv_df") aespca(as.matrix(colonSurv_df[, 5:50])) ## End(Not run)