Find Aliases (Dependencies) in a Model
Usage
alias(object, ...)
Arguments
object
|
A fitted model object, for example from lm or
aov , or a formula for alias.formula .
|
data
|
Optionally,a data frame to search for the objects in the formula.
|
complete
|
Should information on complete aliasing be included?
|
partial
|
Should information on partial aliasing be included?
|
Description
Although the main method is for class "lm"
, alias
is
most useful for experimental designs and so is used with fits from
aov
.
Complete aliasing refers to effects in linear models that cannot be estimated
independently of the terms which occur earlier in the model and so
have their coefficients omitted from the fit. Partial aliasing refers
to effects that can be estimated less precisely because of
correlations induced by the design.Value
A list containing components
Model
|
Description of the model; usually the formula.
|
Complete
|
A matrix with columns corresponding to effects that
are linearly dependent on the rows.
|
Partial
|
The correlations of the estimable effects, with a zero
diagonal.
|
Author(s)
B.D. RipleyExamples
## From Venables and Ripley (1997) p.210.
N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0)
P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0)
K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0)
yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5,
55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0)
# The next line is optional.
library(MASS) # for fractions package which gives neater results.
npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P),
K=factor(K), yield=yield)
npk.aov <- aov(yield ~ block + N*P*K, npk)
alias(npk.aov)