rsm.design
object.
rsm.lm(formula, data, response, ...)
formula.rsm.lm
for a
simple method of specifying the formula.
rsm.design
object in which the variables named in
the formula are to be found. If data is omitted, the current
search list is used to find the objects in formula;
frequently, a data frame will have been attached.
rsm.factor
in
data
is used. This argument is used to specify
the response when
the first argument is a
rsm.design
(See Details for this calling option).
lm
.
rsm.lm
which inherits from
lm
.
It contains all the components returned by
lm
. In addition,
the following components are returned:
lm
fit.
rsm.factors
in the model.
lm
to the coded factors.
This function allows a simpler calling sequence than the
S-PLUS convention of
formula, data
. If the first argument is of
class
rsm.design
, this is treated as the
data
argument. A
formula for fitting a quadratic surface is supplied as the default, by
formula(data)
. The response can be specified
by the
response
argument - by default it is the first non-factor in
data
.
Response surface designs, by convention, are fit with the experimental
factors scaled and centered so that the model coefficients are
independent. This has the advantage that terms can be dropped from the
model without changing the coefficient or t-statistics of the
remaining terms. The variables in an
rsm.design
object are
rsm.factors
, that carry information about scaling and centering that
is used by
rsm.lm
(the function
as.rsm.design
will assist in
converting a
data.frame
to an
rsm.design
object), that is, the
factors are centered and scaled for the
scaledfit
. The
rsm.lm
object also returns the coefficients for the natural scale of the
factors.
If the coded factors are not orthogonal, the coefficients of the model
are not independent, although often nearly so.
Box, G.E.P. and Draper, N.R (1987),
Empirical Model Building and Response
Surfaces, New York: Wiley.
Box, G. E. P., W. G. Hunter, J. S. Hunter (1978),
Statistics for Experimenters,
New York: Wiley.
Haaland, P. D. (1989),
Experimental Design in Biotechnology,
New York: Marcel Dekker.
abrsm.df #The following two calls are equivalent abrsm.rsm <- rsm.lm(abrsm.df) #the S+DOX convention abrsm.rsm2 <- rsm.lm(TtrVol ~ (RadDos + Prime1)^2 + RadDos^2 + Prime1^2, abrsm.df) #The S-PLUS convention summary(abrsm.rsm) pareto(abrsm.rsm) surface(abrsm.rsm) contour(abrsm.rsm) optim(abrsm.rsm)