jingjiang yan <jingjiangyan <at> gmail.com> writes: > > hi, people > How can we compare two probit models brought out from the same data? > Let me use the example used in "An Introduction to R". > "Consider a small, artificial example, from Silvey (1970). > > On the Aegean island of Kalythos the male inhabitants suffer from a > congenital eye disease, the effects of which become more marked with > increasing age. Samples of islander males of various ages were tested for > blindness and the results recorded. The data is shown below: > > Age: 20 35 45 55 70 > No. tested: 50 50 50 50 50 > No. blind: 6 17 26 37 44 > " > > now, we can use the age and the blind percentage to produce a probit model > and get their coefficients by using glm function as was did in "An > Introduction to R" > > My question is, let say there is another potential factor instead of age > affected the blindness percentage. > for example, the height of these males. Using their height, and their > relevant blindness we can introduce another probit model. > > If I want to determine which is significantly better, which function can I > use to compare both models? and, in addition, compared with the Null > hypothesis(i.e. the same blindness for all age/height) to prove this model > is effective? >
You can use a likelihood ratio test (i.e. anova(model1,model0) to compare either model to the null model (blindness is independent of both age and height). The age model and height model are non-nested, and of equal complexity. You can tell which one is *better* by comparing log-likelihoods/deviances, but cannot test a null hypothesis of significance. Most (but not all) statisticians would say you can compare non-nested models by using AIC, but you don't get a hypothesis-test/p-value in this way. Ben Bolker ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
