dr.fit {dr}R Documentation

Fit dimension reduction regression

Description

Internal generic function that estimates the central subspace.

Usage

dr.fit(object, numdir=4, ...)

Arguments

object dimension reduction regression object
numdir maximum number of dimensions to consider
tol tolerance passed to singular value decomposition
... other arguments passed to dr.fit.M

Details

These functions will not typically be called directly by the user. At present, the same dr.fit method works for all dimension reduction methods implemented in this package, but one could potentially write a special dr.fit method if needed.

The general outline of this method is as follows. (1) A matrix M is computed by a call to dr.fit.M(object,...), such that the columns of M are estimated to fall in the subspace of interest (either the central subspace or the central mean subspace). (2) If M is square, its eigenvalues and eigenvectors are computed; if M is not square, the eigenvalues of M'M are computed. (3) M was computed with scaled and centered predictors. The eigenvectors are backtransformed to the original scale.

Value

evectors ordered eigenvectors that describe the estimates of the dimension reduction subspace
evalues ordered eigenvalues
numdir number of eigenvalues
raw.evectors eigenvectors of the rotated data
M The kernel matrix.

Author(s)

Sanford Weisberg <sandy@stat.umn.edu>

See Also

dr


[Package dr version 2.0.3 Index]