alias
Find Aliases (Dependencies) in a Model
Find aliases (linearly dependent terms) in a linear model specified by a formula.
 Keywords
 models
Usage
alias(object, ...)
"alias"(object, data, ...)
"alias"(object, complete = TRUE, partial = FALSE, partial.pattern = FALSE, ...)
Arguments
 object
 A fitted model object, for example from
lm
oraov
, or a formula foralias.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?
 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
\href{https://CRAN.Rproject.org/package=#1}{\pkg{#1}}MASSMASS to be installed.
Value

A list (of
 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
"mtable"
which has its ownprint
method.
class
"listof"
) containing components
Note
The aliasing pattern may depend on the contrasts in use: Helmert contrasts are probably most useful.
The defaults are different from those in S.
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.