aespca {pathwayPCA} | R Documentation

## Adaptive, elastic-net, sparse principal component analysis

### 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)

[Package pathwayPCA version 1.6.3 Index]