R: Rotate a Matrix of Component Loadings using the VARIMAX...

varmx {fda} | R Documentation

## Rotate a Matrix of Component Loadings using the VARIMAX Criterion

### Description

The matrix being rotated contains the values of the component functional data objects computed in either a principal components analysis or a canonical correlation analysis. The values are computed over a fine mesh of argument values.

### Usage

    varmx(amat, normalize=FALSE)

### Arguments

`amat` | the matrix to be rotated. The number of rows is equal to the number of argument values `nx` used in a fine mesh. The number of columns is the number of components to be rotated.
`normalize` | either `TRUE` or `FALSE`. If `TRUE`, the columns of `amat` are normalized prior to computing the rotation matrix. However, this is seldom needed for functional data.

### Details

The VARIMAX criterion is the variance of the squared component values. As this criterion is maximized with respect to a rotation of the space spanned by the columns of the matrix, the squared loadings tend more and more to be either near 0 or near 1, and this tends to help with the process of labelling or interpreting the rotated matrix.

### Value

a square rotation matrix of order equal to the number of components that are rotated. A rotation matrix $T$ has that property that $T'T = TT' = I$.

### See Also

`varmx.pca.fd`, `varmx.cca.fd`

[Package fda version 2.4.8 Index]