Dimension reduction regression returns a set of p orthogonal direction vectors each of length p, the first d of which are estimates a basis of a d dimensional central subspace. The function returns the estimated directions in the original n dimensional space for plotting.
dr.direction(object, which, norm, x) dr.directions(object, which, norm, x) dr.direction.default(object, which=1:object$numdir,norm=F,x=dr.x(object))
object |
a dimension reduction regression object |
which |
select the directions wanted, default is all directions |
norm |
if TRUE, direction vectors are normalized to length 1, otherwise their length is arbitrary |
x |
select the X matrix, the default is dr.x(object) |
... |
additional arguments are passed to dr.direction.default |
Dimension reduction regression is used to estimate a basis of the central subspace of a regression. If there are p predictors, the dimension reduction regression object includes a p by p matrix of C of eigenvectors. This method returns (X-m1')C where m is the vector of column means of X. If X is equal to the original matrix of predictors given by dr.x(object), then this gives the directions in the coordinates of the orginal n dimensional space. These directions are used in graphical methods and elsewhere.
Returns a matrix. The same function has two names.
Sanford Weisberg <sandy@stat.umn.edu>
See R. D. Cook (1998). Regression Graphics. New York: Wiley.
library(dr) data(ais) attach(ais) # the Australian athletes data #fit dimension reduction using sir m1 <- dr(LBM~Wt+Ht+RCC+WCC, method="sir", nslices = 8) summary(m1) dr.directions(m1)