文档介绍：JSS Journal of Statistical Software
October 2009, Volume 32, Issue 7. /
Response-Surface Methods in R, Using rsm
Russell V. Lenth
The University of Iowa
Abstract
This article describes the recent package rsm, which was designed to provide R sup-
port for standard response-surface methods. Functions are provided to generate central-
composite and Box-Behnken designs. For analysis of the resulting data, the package
provides for estimating the response surface, testing its lack of fit, displaying an ensemble
of contour plots of the fitted surface, and doing follow-up analyses such as steepest ascent,
canonical analysis, and ridge analysis. It also implements a coded-data structure to aid in
this essential aspect of the methodology. The functions are designed in hopes of providing
an intuitive and effective user interface. Potential exists for expanding the package in a
variety of ways.
Keywords: response-surface methods, regression, experimental design, first-order designs,
second-order designs.
1. Introduction
Response-surface prises a body of methods for exploring for optimum op-
erating conditions through experimental methods. Typically, this involves doing several ex-
periments, using the results of one experiment to provide direction for what to do next. This
next action could be to focus the experiment around a different set of conditions, or to collect
more data in the current experimental region in order to fit a higher-order model or confirm
what we seem to have found.
Different levels or values of the operating prise the factors in each experiment.
Some may be categorical (., the supplier of raw material) and others may be quantitative
(feed rates, temperatures, and such). In practice, categorical variables must be handled sepa-
rately paring our best operating conditions with respect to the quantitative variables
across binations of the categorical ones. The fundamental methods for quanti-
tative variables involve fittin