Tord Snall <[EMAIL PROTECTED]> writes: > Null deviance: 13.1931 on 269 degrees of freedom > Residual deviance: 9.9168 on 268 degrees of freedom > AIC: 13.917 ...
> BUT, note the under dispersion. I GUESS it is because I have surveyed a > moss on marked trees at three occations (with two years in between). The > response 1 means that the moss has disappeared, and dbh is tree diameter. > (This corresponds to revisitng patients who has a disease, and whose weight > is unchanged between the visits. H0: weight does not affect tha chance of > recovery from the disease) Don't trust deviances as measures of dispersion with binary data! > Here is a version with quasibinomial: > ... > > Note, no warning. > > I guess that this quasibinomial model is more reliable than the binomial. > Now I can trust the SE of the Estim. too, can't I? No. Neither nor. With binary data, the deviance is purely a function of the fitted parameters. It is the difference in -2 log L between a "perfect fit" and the observed fit. A perfect fit has a zero prob. where the obs is "0" and probability 1 where it is "1", and L == 1 identically in that case. Now consider the likelihood for the "complete toss-up" i.e. intercept and slope both equal to 0 so all probabilities are 0.5. The likelihood in that case is 0.5^269, i.e. a constant. Take logarithms and notice that the model deviance plus the change in deviance from the model to the "toss-up" model is constant (2*269*log(2) to be precise). So what appears to be a measure of residual error is really just a measure of how far the fitted probabilities are from 0.5! -- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help