R: compute various pseudo-R2 measures

pR2 {pscl} | R Documentation

## compute various pseudo-R2 measures

### Description

compute various pseudo-R2 measures for various GLMs

### Usage

    pR2(object, ...)

### Arguments

`object` | a fitted model object, for now of class `glm`, `polr`, or `mulitnom`
`...` | additional arguments to be passed to or from functions

### Details

Numerous pseudo r-squared measures have been proposed for generalized linear models, involving a comparison of the log-likelihood for the fitted model against the log-likelihood of a null/restricted model with no predictors, normalized to run from zero to one as the fitted model provides a better fit to the data (providing a rough analogue to the computation of r-squared in a linear regression).

### Value

A vector of length 6 containing

`llh` | The log-likelihood from the fitted model
`llhNull` | The log-likelihood from the intercept-only restricted model
`G2` | Minus two times the difference in the log-likelihoods
`McFadden` | McFadden's pseudo r-squared
`r2ML` | Maximum likelihood pseudo r-squared
`r2CU` | Cragg and Uhler's pseudo r-squared

### Author(s)

Simon Jackman simon.jackman@sydney.edu.au

### References

Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Sage. pp104-106.

### See Also

`extractAIC`, `logLik`

### Examples

    data(admit)
    ## ordered probit model
    op1 <- MASS::polr(score ~ gre.quant + gre.verbal + ap + pt + female,
                Hess=TRUE,
                data=admit,
                method="probit")
    pR2(op1)

[Package pscl version 1.5.2 Index]