R: Adjusted R-square

RsquareAdj {vegan} | R Documentation

## Adjusted R-square

### Description

The functions finds the adjusted R-square.

### Usage

    ## Default S3 method:
    RsquareAdj(x, n, m, ...)
    ## S3 method for class 'rda'
    RsquareAdj(x, ...)
    ## S3 method for class 'cca'
    RsquareAdj(x, permutations = 1000, ...)

### Arguments

`x` | Unadjusted R-squared or an object from which the terms for evaluation or adjusted R-squared can be found.
`n, m` | Number of observations and number of degrees of freedom in the fitted model.
`permutations` | Number of permutations to use when computing the adjusted R-squared for a cca. The permutations can be calculated in parallel by specifying the number of cores which is passed to `permutest`
`...` | Other arguments (ignored) except in the case of cca in which these arguments are passed to `permutest`.

### Details

The default method finds the adjusted R-squared from the unadjusted R-squared, number of observations, and number of degrees of freedom in the fitted model. The specific methods find this information from the fitted result object. There are specific methods for `rda`, `cca`, `lm` and `glm`. Adjusted, or even unadjusted, R-squared may not be available in some cases, and then the functions will return `NA`. There is no adjusted in partial `rda`, and R-squared values are available only for `gaussian` models in `glm`.

The adjusted, R-squared of `cca` is computed using a permutation approach developed by Peres-Neto et al. (2006). By default 1000 permutations are used.

The raw R-squared of partial `rda` gives the proportion explained after removing the variation due to conditioning (partial) terms; Legendre et al. (2011) call this semi-partial R-squared. The adjusted R-squared is found as the difference of adjusted R-squared values of joint effect of partial and constraining terms and partial term alone, and it is the same as the adjusted R-squared of component `[a] = X1|X2` in two-component variation partition in `varpart`.

### Value

The functions return a list of items `r.squared` and `adj.r.squared`.

### References

Legendre, P., Oksanen, J. and ter Braak, C.J.F. (2011). Testing the significance of canonical axes in redundancy analysis. Methods in Ecology and Evolution 2, 269–277.

Peres-Neto, P., P. Legendre, S. Dray and D. Borcard. 2006. Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology 87, 2614–2625.

### See Also

`varpart` uses `RsquareAdj`.

### Examples

    data(mite)
    data(mite.env)
    ## rda
    m <- rda(decostand(mite, "hell") ~  ., mite.env)
    RsquareAdj(m)
    ## cca
    m <- cca(decostand(mite, "hell") ~  ., mite.env)
    RsquareAdj(m)
    ## default method
    RsquareAdj(0.8, 20, 5)

[Package vegan version 2.4-3 Index]