[R] R-squared in Logistic Regression

From: Johan Stenberg <jstenberg_at_ice.mpg.de>
Date: Tue 29 Mar 2005 - 18:56:06 EST


Dear all,

How do I make R show the R-squared (deviance explained by the model) in a logistic regression?

Below is how I write my syntax. Basically I want to investigate density-dependence in parasitism of larvae. Note that in the end I perform a F-test because the dispersion factor (residual deviance / residual df) is significantly higher than 1. But how do I make R show the "R-squared"?

Best wishes
Johan

> y<-cbind(para,unpara)
> model<-glm(y~log(larvae),binomial)
> summary(model)

Call:
glm(formula = y ~ log(larvae), family = binomial)

Deviance Residuals:

    Min 1Q Median 3Q Max -2.0633 -1.6218 -0.1871 0.7907 2.7670

Coefficients:

            Estimate Std. Error z value Pr(>|z|)
(Intercept)   1.0025     0.7049   1.422  0.15499
log(larvae)  -1.0640     0.3870  -2.749  0.00597 **

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 35.981 on 12 degrees of freedom Residual deviance: 27.298 on 11 degrees of freedom AIC: 40.949 Number of Fisher Scoring iterations: 4

> anova(model,test="F")
Analysis of Deviance Table

Model: binomial, link: logit

Response: y

Terms added sequentially (first to last)

            Df Deviance Resid. Df Resid. Dev      F   Pr(>F)
NULL                           12     35.981
log(larvae)  1    8.683        11     27.298 8.6828 0.003212 **

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