# 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, …)`# S3 method for formula
alias(object, data, …)

# S3 method for lm
alias(object, complete = TRUE, partial = FALSE,
partial.pattern = FALSE, …)

##### 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?

- 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

Description of the model; usually the formula.

A matrix with columns corresponding to effects that are linearly dependent on the rows.

The correlations of the estimable effects, with a zero
diagonal. An object of class `"mtable"`

which has its own
`print`

method.

##### 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.

##### Examples

`library(stats)`

```
# NOT RUN {
<!-- % as it loads MASS -->
op <- options(contrasts = c("contr.helmert", "contr.poly"))
npk.aov <- aov(yield ~ block + N*P*K, npk)
alias(npk.aov)
options(op) # reset
# }
```

*Documentation reproduced from package stats, version 3.5.0, License: Part of R 3.5.0*