FO | 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 (..., formula)
PQ (...)
SO (...)
PE (...)
... |
The numerical predictors for the response surface, separated by commas. |
formula |
Alternative way to specify two-way interactions. Use |
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 never 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 formula
argument in TWI
can simplify specifying models where only certain interactions are included. For example, ‘TWI(formula = ~x1:(x2+x3))’ is equivalent to ‘TWI(x1,x2) + TWI(x1,x3)’. The formula is expanded using terms
, and then only the second-order terms are retained. If this results in only one term, an error condition is raised. This is necessary to prevent rsm
from getting confused in identifying second-order terms.
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.
Russell V. Lenth
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")}
rsm
### See 'rsm' help for examples of FO, TWI, etc
library(rsm)
### Test LOF for a regression model
ChemReact.lm <- lm(Yield ~ Time*Temp, data = ChemReact1)
PureError.lm <- update (ChemReact.lm, . ~ PE(Time,Temp))
anova (ChemReact.lm, PureError.lm)
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