FO {rsm} | R Documentation |
Use of one of these functions in a model is how you specify the portion of the model that is to be regarded as a response-surface component.
FO (...) TWI (...) PQ (...) SO (...) PE (...)
... |
The numerical predictors for the response surface, separated by commas. |
Use FO()
in the model formula in rsm
to specify a first-order response surface (i.e., a linear function)
in its arguments. Use TWI()
to generate two-way interactions, and PQ()
to generate
pure quadratic terms (squares of the FO()
terms). A call to
SO()
creates all terms in FO()
, TWI()
, and PQ()
(in that order) for those
variables. However, specifying SO()
in a model formula in rsm
will be replaced
by the explicit sum of model terms, so that the anova
table shows separate sums of squares.
Other variables (such as blocks or factors) may be included in the model
but should nevel be included in the arguments to FO
or SO
.
PE
is used for fitting pure-error models. It should not be used in
response-surface models. This function exists primarily for use
by loftest
, but could be useful in other linear-model
contexts for fitting a model that interpolates the means at each distinct
combination of argument values.
The functions FO
, TWI
, PQ
, and SO
return a matrix whose
columns are the required predictors.
PE
returns a factor
whose levels are all the distinct combinations of
arguments provided to the function.
Russ Lenth
### See 'rsm' help for more examples library(rsm) ### Test LOF for a regression model ChemReact.lm = lm(Yield ~ Time*Temp, data=ChemReact, subset=1:7) PureError.lm = update (ChemReact.lm, . ~ PE(Time,Temp)) anova (ChemReact.lm, PureError.lm)