optim.rsm.lm(rsm.object, best = "max", bounded = T,
constrained, ...)
rsm.lm.
"max") or minimum (
"min").
TRUE, the default, the optimization is performed within the
range of the settings of the experimental factors, if
FALSE the upper and
lower bounds used in optimization are
Inf and
- Inf. If
constrained is
given, those bounds are used instead.
rsm.object, which specifies whether each
variable is fitted on the log scale in the
rsm model.
rsm.object
.
If a variable is fitted on the log scale (for instance, if the design
is generated using
rsm.design, and log = T) in
rsm.lm, the
constraints are assumed to be specified on the natural scale. For
example, if the design points on the (fitting) log scale for variable
X
are
c(log(0.1), log(1), log(10))
, the optimization is constrained to the
center point by setting
constrained = list(X = 1).
ab.rsm <- rsm.lm(formula(abrsm.df), abrsm.df)
optim(ab.rsm)
optim(ab.rsm, constrained = list(Prime1 = 14))
optim(ab.rsm, constrained = list(RadDos = c(150,200),
Prime1 = c(-Inf, 14)))