R-squared in Logistic Regression

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 **