rsm-package: Response-surface analysis
In rsm: Response-Surface Analysis
rsm-package R Documentation
Response-surface analysis
Description
The rsm package provides functions useful for designing and analyzing
experiments that are done sequentially in hopes of optimizing a response surface.
The function ccd can generate (and randomize) a central-composite
design; it allows the user to specify an aliasing or fractional blocking structure.
The function bbd generates and randomizes a Box-Behnken design.
The function ccd.pick is useful for identifying good parameter choices
in central-composite designs. Functions cube, star, foldover, dupe, and djoin are also provided to build-up designs from individual blocks. The function varfcn allows the experimenter to examine the predictive capabilities of a design before collecting data.
The function rsm is an enhancement of lm that provides
for additional analyses peculiar to response surfaces. It requires a model formula
that contains a call to FO or SO to specify a first- or
second-order model. Once the model is fitted, the steepest
function may be used to obtain the direction of steepest ascent (or descent).
canonical.path is an alternative to steepest for second-order
response surfaces.
In RSM methods, appropriate coding of data is
important not only for numerical stability, but for proper scaling
of results; the function coded.data and its relatives facilitate
this coding requirement.
Finally, a few more functions are provided that may be useful beyond response-surface applications.
contour.lm, persp.lm, and image.lm aids in visualizing a response surface,
or of any other lm object where a surface is fitted. model.data
recovers the data used in a lm call, but unlike model.frame, no
polynomials, factors, etc. are expanded.
For more information and examples, use ‘vignette("rsm")’ and ‘vignette("rs-illus")’.
Additionally, ‘vignette("rsm-plots")’ provides some illustrations
of the graphics functions.
Author(s)
Russell V. Lenth
Maintainer: Russell V. Lenth <russell-lenth@uiowa.edu>
References
Box, GEP, Hunter, JS, and Hunter, WG (2005)
Statistics for Experimenters (2nd ed.), Wiley-Interscience.
Lenth RV (2009) “Response-Surface Methods in R, Using rsm”,
Journal of Statistical Software, 32(7), 1–17.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v032.i07")}
Myers, RH, Montgomery, DC, and Anderson-Cook, CM (2009),
Response Surface Methodology (3rd ed.), Wiley.
rsm documentation built on June 22, 2024, 9:42 a.m.
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| rsm-package | R Documentation |
Response-surface analysis
Description
The rsm package provides functions useful for designing and analyzing
experiments that are done sequentially in hopes of optimizing a response surface.
The function ccd can generate (and randomize) a central-composite
design; it allows the user to specify an aliasing or fractional blocking structure.
The function bbd generates and randomizes a Box-Behnken design.
The function ccd.pick is useful for identifying good parameter choices
in central-composite designs. Functions cube, star, foldover, dupe, and djoin are also provided to build-up designs from individual blocks. The function varfcn allows the experimenter to examine the predictive capabilities of a design before collecting data.
The function rsm is an enhancement of lm that provides
for additional analyses peculiar to response surfaces. It requires a model formula
that contains a call to FO or SO to specify a first- or
second-order model. Once the model is fitted, the steepest
function may be used to obtain the direction of steepest ascent (or descent).
canonical.path is an alternative to steepest for second-order
response surfaces.
In RSM methods, appropriate coding of data is
important not only for numerical stability, but for proper scaling
of results; the function coded.data and its relatives facilitate
this coding requirement.
Finally, a few more functions are provided that may be useful beyond response-surface applications.
contour.lm, persp.lm, and image.lm aids in visualizing a response surface,
or of any other lm object where a surface is fitted. model.data
recovers the data used in a lm call, but unlike model.frame, no
polynomials, factors, etc. are expanded.
For more information and examples, use ‘vignette("rsm")’ and ‘vignette("rs-illus")’. Additionally, ‘vignette("rsm-plots")’ provides some illustrations of the graphics functions.
Author(s)
Russell V. Lenth
Maintainer: Russell V. Lenth <russell-lenth@uiowa.edu>
References
Box, GEP, Hunter, JS, and Hunter, WG (2005) Statistics for Experimenters (2nd ed.), Wiley-Interscience.
Lenth RV (2009) “Response-Surface Methods in R, Using rsm”, Journal of Statistical Software, 32(7), 1–17. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v032.i07")}
Myers, RH, Montgomery, DC, and Anderson-Cook, CM (2009), Response Surface Methodology (3rd ed.), Wiley.
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