aespca: Adaptive, elastic-net, sparse principal component analysis

In pathwayPCA: Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection

Description

A function to perform adaptive, elastic-net, sparse principal component analysis (AES-PCA).

Usage

aespca(X, d = 1, max.iter = 10, eps.conv = 0.001, adaptive = TRUE, para = NULL)

Arguments

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 while() loop can make calls to the lars.lsa() function. Defaults to 10.
eps.conv	A numerical convergence threshold for the same while() loop. Defaults to 0.001.
adaptive	Internal argument of the lars.lsa() function. Defaults to TRUE.
para	Internal argument of the lars.lsa() function. Defaults to NULL.

Details

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.

Value

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.

See Also

normalize; lars.lsa; ExtractAESPCs; AESPCA_pVals

Examples

  # 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)

pathwayPCA documentation built on Dec. 15, 2020, 6:14 p.m.