文章基本信息

标题：Dimension Reduction Regression in R
本地全文：下载
作者：Sanford Weisberg
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2002
卷号：7
期号：1
页码：1-22
语种：English
出版社：University of California, Los Angeles
摘要：Regression is the study of the dependence of a response variable y on a collection predictors p collected in x. In dimension reduction regression, we seek to find a few linear combinations β 1 x,...,β d x, 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=1, the plot of y versus β 1 x contains all the regression information. When d=2, a 3D plot contains all the information. Several methods for estimating d and relevant functions of β 1 ,..., β d 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 , estimates of the relevant information including β 1 ,..., β d , and some useful graphical summaries as well.