PCA-methods {Cardinal} | R Documentation

## Principal Components Analysis

### Description

Performs principal components analysis efficiently on large datasets using implicitly restarted Lanczos bi-diagonalization (IRLBA) algorithm for approximate singular value decomposition of the data matrix.

### Usage

        ## S4 method for signature 'SImageSet'
    PCA(x, ncomp = 20,
            method = c("irlba", "nipals", "svd"),
            center = TRUE,
            scale = FALSE,
            iter.max = 100, ...)

        ## S4 method for signature 'PCA'
    predict(object, newx, ...)

### Arguments

`x` | The imaging dataset for which to calculate the principal components.
`ncomp` | The number of principal components to calculate.
`method` | The function used to calculate the singular value decomposition.
`center` | Should the data be centered first? This is passed to `scale`.
`scale` | Shoud the data be scaled first? This is passed to `scale`.
`iter.max` | The number of iterations to perform for the NIPALS algorithm.
`...` | Ignored.
`object` | The result of a previous call to `PCA`.
`newx` | An imaging dataset for which to calculate the principal components scores based on the aleady-calculated principal components loadings.

### Value

An object of class `PCA`, which is a `ResultSet`, where each component of the `resultData` slot contains at least the following components:

`scores`:

  A matrix with the principal component scores.

`loadings`:

  A matrix with the principal component loadings.

`sdev`:

  The standard deviations of the principal components.

`method`:

  The method used to calculate the principal components.

`ncomp`:

  The number of principal components calculated.

`center`:

  The center of the dataset. Used for calculating principal components scores on new data.

`scale`:

  The scaling factors for the dataset. Used for calculating principal components scores on new data.

### Author(s)

Kylie A. Bemis

### See Also

`OPLS`, `PLS`, `irlba`, `svd`

### Examples

    sset <- generateImage(diag(4), range=c(200, 300), step=1)

    pca <- PCA(sset, ncomp=2)

[Package Cardinal version 1.12.1 Index]