Christopher Manning's writeup on logistic regression in R shows a logistic regression in R as follows:
ced.logr <- glm(ced.del ~ cat + follows + factor(class), family=binomial)
> summary(ced.logr) Call: glm(formula = ced.del ~ cat + follows + factor(class), family = binomial("logit")) Deviance Residuals: Min 1Q Median 3Q Max -3.24384 -1.34325 0.04954 1.01488 6.40094 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.31827 0.12221 -10.787 < 2e-16 catd -0.16931 0.10032 -1.688 0.091459 catm 0.17858 0.08952 1.995 0.046053 catn 0.66672 0.09651 6.908 4.91e-12 catv -0.76754 0.21844 -3.514 0.000442 followsP 0.95255 0.07400 12.872 < 2e-16 followsV 0.53408 0.05660 9.436 < 2e-16 factor(class)2 1.27045 0.10320 12.310 < 2e-16 factor(class)3 1.04805 0.10355 10.122 < 2e-16 factor(class)4 1.37425 0.10155 13.532 < 2e-16 (Dispersion parameter for binomial family taken to be 1) Null deviance: 958.66 on 51 degrees of freedom Residual deviance: 198.63 on 42 degrees of freedom AIC: 446.10 Number of Fisher Scoring iterations: 4
He then goes into some detail about how to interpret coefficients, compare different models, and so on. Quite useful.
However, how much variance does the model account for? A Stata page on logistic regression says:
Technically, R2 cannot be computed the same way in logistic regression as it is in OLS regression. The pseudo-R2, in logistic regression, is defined as (1 - L1)/L0, where L0 represents the log likelihood for the "constant-only" model and L1 is the log likelihood for the full model with constant and predictors.
I understand that at the high level. The constant-only model would be without any of the parameters (only the intercept term). Log likelihood is a measure of how closely the parameters fit the data. In fact, Manning sort of hints that the deviance might be -2 log L. Perhaps null deviance is constant-only and residual deviance is -2 log L of the model? However, I'm not crystal clear on it.
Can someone verify how one actually computes the pseudo-R^2 in R using this example?