rsq.n: Corrected Likelihood-Ratio-Based R-Squared

In rsq: R-Squared and Related Measures

Corrected Likelihood-Ratio-Based R-Squared

Description

Corrected likelihood-ratio-based R^2 for generalized linear models.

Usage

rsq.n(fitObj,adj=FALSE)

Arguments

fitObj	an object of class "lm" or "glm", usually, a result of a call to lm, glm, or glm.nb.
adj	logical; if TRUE, calculate the adjusted R^2.

Details

Nagelkerke (1991) proposed this version of R^2 to correct the likelihood-ratio-statistic-based one which was proposed by Maddala (1983), Cox and Snell (1989), and Magee (1990). This corrected generalization of R^2 cannot reduce to the classical R^2 in case of linear models. It is not defined for quasi models.

Value

The R^2 or adjusted R^2.

Author(s)

Dabao Zhang, Department of Statistics, Purdue University

References

Cox, D. R. and Snell, E. J. (1989) The Analysis of Binary Data, 2nd ed. London: Chapman and Hall.

Maddala, G. S. (1983) Limited-Dependent and Qualitative Variables in Econometrics. Cambridge University.

Magee, L. (1990) R^2 measures based on Wald and likelihood ratio joint significance tests. The American Statistician, 44: 250-253.

Nagelkerke, N. J. D. (1991) A note on a general definition of the coefficient of determination. Biometrika, 78: 691-692.

See Also

rsq, rsq.partial, pcor, rsq.lr.

Examples

data(hcrabs)
attach(hcrabs)
y <- ifelse(num.satellites>0,1,0)
bnfit <- glm(y~color+spine+width+weight,family=binomial)
rsq.n(bnfit)
rsq.n(bnfit,adj=TRUE)

psfit <- glm(num.satellites~color+spine+width+weight,family=poisson)
rsq.n(psfit)
rsq.n(psfit,adj=TRUE)

# Effectiveness of Bycycle Safety Helmets in Thompson et al. (1989)
y <- matrix(c(17,218,233,758),2,2)
x <- factor(c("yes","no"))
tbn <- glm(y~x,family=binomial)
rsq.n(tbn)
rsq.n(tbn,adj=TRUE)

rsq documentation built on Oct. 22, 2023, 5:07 p.m.