Description Usage Arguments Details Value Author(s) References See Also Examples

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.

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`...` |
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.
doi: 10.18637/jss.v032.i07

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