TY - JOUR
T1 - Dimension reduction regression in R
AU - Weisberg, Sanford
PY - 2002
Y1 - 2002
N2 - Regression is the study of the dependence of a response variable y on a collection p predictors collected in x. In dimension reduction regression, we seek to find a few linear combinations β′1x,..., β′dx, such that all the information about the regression is contained in these linear combinations. If d is very small, perhaps one or two, then the regression problem can be summarized using simple graphics; for example, for d, the plot of y versus β′1x contains all the regression information. When d=2, a 3D plot contains all the information. Several methods for estimating d and relevant functions of β1..., βd have been suggested in the literature. In this paper, we describe an R package for three important dimension reduction methods: sliced inverse regression or sir, sliced average variance estimates, or save, and principal Hessian directions, or phd. The package is very general and flexible, and can be easily extended to include other methods of dimension reduction. It includes tests and estimates of the dimension d, estimates of the relevant information including β1...,βd, and some useful graphical summaries as well.
AB - Regression is the study of the dependence of a response variable y on a collection p predictors collected in x. In dimension reduction regression, we seek to find a few linear combinations β′1x,..., β′dx, such that all the information about the regression is contained in these linear combinations. If d is very small, perhaps one or two, then the regression problem can be summarized using simple graphics; for example, for d, the plot of y versus β′1x contains all the regression information. When d=2, a 3D plot contains all the information. Several methods for estimating d and relevant functions of β1..., βd have been suggested in the literature. In this paper, we describe an R package for three important dimension reduction methods: sliced inverse regression or sir, sliced average variance estimates, or save, and principal Hessian directions, or phd. The package is very general and flexible, and can be easily extended to include other methods of dimension reduction. It includes tests and estimates of the dimension d, estimates of the relevant information including β1...,βd, and some useful graphical summaries as well.
UR - http://www.scopus.com/inward/record.url?scp=4544378893&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=4544378893&partnerID=8YFLogxK
U2 - 10.18637/jss.v007.i01
DO - 10.18637/jss.v007.i01
M3 - Article
AN - SCOPUS:4544378893
SN - 1548-7660
VL - 7
SP - 1
EP - 22
JO - Journal of Statistical Software
JF - Journal of Statistical Software
ER -