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
Value
evectorsordered eigenvectors that describe the estimates of the
dimension reduction subspace
evaluesordered eigenvalues
numdirnumber of eigenvalues
raw.evectorseigenvectors of the rotated data
MThe kernel matrix.
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.