dr.fit {dr} | R Documentation |
Internal generic function that estimates the central subspace.
dr.fit(object, numdir=4, ...)
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 |
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.
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. |
Sanford Weisberg <sandy@stat.umn.edu>