formula.design: Function to change the default formula for a data frame of class design to involve the correct factors with the desired effects and responses

Description

This function provides a reasonable default formula for linear model analyses of class design objects with response(s). Per default, the resulting formula refers to the first response in the design and is of design-type specific nature.

Usage

## S3 method for class 'design':
formula(x, ..., response=NULL, degree=NULL, FUN=NULL, 
      use.center=TRUE)

Arguments

an object of class design

...

further arguments to function formula

response

character string giving the name of the response variable (must be among the numeric columns from x) OR integer number giving the position of the response in element response.names of attribute desi

degree

degree of the model (1=main effects only, 2=with 2-factor interactions and quadratic effects, 3=with 3-factor interactions and up to cubic effects, ...

FUN

function for the aggregate.design method; this must be an unquoted function name or NULL; This option is relevant for repeated measurement designs and parameter des

use.center

logical indicating whether center points are to be used + in the analysis; relevant for pb and FrF2 designs with center points only

Value

a formula

code

lm.design

Details

Function formula creates an appropriate formula for many kinds of objects, e.g. for data frames (try e.g. formula(swiss)). Function as.formula uses function formula, but cannot take any additional arguments. The method for class design objects modifies the way a data frame would normally be treated by the formula function. This also carries through to default linear models. Without the additional arguments, the function creates the formula with the first response from the response.names element of the design.info attribute. The default degree depends on the type of design: it is

{1 for oa and pb} {2 for all other design types}

Examples

Run this code

## indirect usage via function lm.design is much more interesting
  ## cf help for lm design!

   my.L18 <- oa.design(ID=L18, 
       factor.names = c("one","two","three","four","five","six","seven"), 
       nlevels=c(3,3,3,2,3,3,3))
   y <- rnorm(18)
   my.L18 <- add.response(my.L18, y)
   formula(my.L18)
   lm(my.L18)

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