R: Statistics Resembling Deviance and AIC for Constrained...

deviance.cca {vegan} | R Documentation

## Statistics Resembling Deviance and AIC for Constrained Ordination

### Description

The functions extract statistics that resemble deviance and AIC from the result of constrained correspondence analysis `cca` or redundancy analysis `rda`. These functions are rarely needed directly, but they are called by `step` in automatic model building. Actually, `cca` and `rda` do not have `AIC` and these functions are certainly wrong.

### Usage

    ## S3 method for class 'cca'
    deviance(object, ...)

    ## S3 method for class 'cca'
    extractAIC(fit, scale = 0, k = 2, ...)

### Arguments

`object` | the result of a constrained ordination (`cca` or `rda`).
`fit` | fitted model from constrained ordination.
`scale` | optional numeric specifying the scale parameter of the model, see `scale` in `step`.
`k` | numeric specifying the "weight" of the equivalent degrees of freedom (=:`edf`) part in the AIC formula.
`...` | further arguments.

### Details

The functions find statistics that resemble `deviance` and `AIC` in constrained ordination. Actually, constrained ordination methods do not have a log-Likelihood, which means that they cannot have AIC and deviance. Therefore you should not use these functions, and if you use them, you should not trust them. If you use these functions, it remains as your responsibility to check the adequacy of the result.

The deviance of `cca` is equal to the Chi-square of the residual data matrix after fitting the constraints. The deviance of `rda` is defined as the residual sum of squares. The deviance function of `rda` is also used for `capscale`. Function `extractAIC` mimics `extractAIC.lm` in translating deviance to AIC.

There is little need to call these functions directly. However, they are called implicitly in `step` function used in automatic selection of constraining variables. You should check the resulting model with some other criteria, because the statistics used here are unfounded. In particular, the penalty `k` is not properly defined, and the default `k = 2` is not justified theoretically. If you have only continuous covariates, the `step` function will base the model building on magnitude of eigenvalues, and the value of `k` only influences the stopping point (but the variables with the highest eigenvalues are not necessarily the most significant in permutation tests in `anova.cca`). If you also have multi-class factors, the value of `k` will have a capricious effect in model building. The `step` function will pass arguments to `add1.cca` and `drop1.cca`, and setting `test = "permutation"` will provide permutation tests of each deletion and addition which can help in judging the validity of the model building.

### Value

The `deviance` functions return “deviance”, and `extractAIC` returns effective degrees of freedom and “AIC”.

### Note

These functions are unfounded and untested and they should not be used directly or implicitly. Moreover, usual caveats in using `step` are very valid.

### Author(s)

Jari Oksanen

### References

Godínez-Domínguez, E. & Freire, J. (2003) Information-theoretic approach for selection of spatial and temporal models of community organization. Marine Ecology Progress Series 253, 17–24.

### See Also

`cca`, `rda`, `anova.cca`, `step`, `extractAIC`, `add1.cca`, `drop1.cca`.

### Examples

    # The deviance of correspondence analysis equals Chi-square
    data(dune)
    data(dune.env)
    chisq.test(dune)
    deviance(cca(dune))
    # Stepwise selection (forward from an empty model "dune ~ 1")
    ord <- cca(dune ~ ., dune.env)
    step(cca(dune ~ 1, dune.env), scope = formula(ord))

[Package vegan version 2.4-3 Index]