pR2: compute various pseudo-R2 measures

In pscl: Political Science Computational Laboratory

compute various pseudo-R2 measures

Description

compute various pseudo-R2 measures for various GLMs

Usage

pR2(object, ...)

Arguments

object	a fitted model object for which logLik, update, and model.frame methods exist (e.g., an object of class glm, polr, or multinom)
...	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 [e-mail]

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)   

pscl documentation built on May 29, 2024, 9:09 a.m.