| alias {stats} | R Documentation |
Find Aliases (Dependencies) in a Model
Description
Find aliases (linearly dependent terms) in a linear model specified by a formula.
Usage
alias(object, ...)
## S3 method for class 'formula'
alias(object, data, ...)
## S3 method for class 'lm'
alias(object, complete = TRUE, partial = FALSE,
partial.pattern = FALSE, ...)
Arguments
object |
A fitted model object, for example from |
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? |
partial.pattern |
Should partial aliasing be presented in a schematic way? If this is done, the results are presented in a more compact way, usually giving the deciles of the coefficients. |
... |
further arguments passed to or from other methods. |
Details
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.
Some parts of the "lm" method require recommended package
MASS to be installed.
Value
A list (of class "listof") 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. An object of class |
Note
The aliasing pattern may depend on the contrasts in use: \IHelmert contrasts are probably most useful.
The defaults are different from those in S.
Author(s)
The design was inspired by the S function of the same name described in Chambers et al. (1992).
References
Chambers, J. M., Freeny, A and Heiberger, R. M. (1992) Analysis of variance; designed experiments. Chapter 5 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
Examples
op <- options(contrasts = c("contr.helmert", "contr.poly"))
npk.aov <- aov(yield ~ block + N*P*K, npk)
alias(npk.aov)
options(op) # reset