Mailing this twice ain't going to help you. Reading a course on statistics might.
The test you want to do is answering following hypothesis : The mean predicted value of a specific model differs when different datasets are used to fit it. Seems likely to me if the datasets are not almost identical. Why testing? About that Z-test : that should be used in your field of research to test 2 proportions that are not too close to 0 or 1 and that originate from a binomial distribution with large enough n. Suggesting to use it for comparing a number of series of around 20 logit-transformed predicted probabilities is plain shocking. In case you are interested in the difference of the intercept for these specific trials, add trial as a fixed effect to your model and do the appropriate testing. You want to know whether the relation between state and days differs in slope, you add an interaction term and again use the appropriate testing. To know what is the appropriate testing, see line 1. Cheers Joris On Thu, Jun 3, 2010 at 10:31 AM, Sacha Viquerat <sacha.v...@googlemail.com>wrote: > dear list! > i have run several glm analysises to estimate a mean rate of dung decay for > independent trials. i would like to compare these results statistically but > can't find any solution. the glm calls are: > > dung.glm1<-glm(STATE~DAYS, data=o_cov, family="binomial(link="logit")) > > dung.glm2<-glm(STATE~DAYS, data=o_cov_T12, family="binomial(link="logit")) > > as all the trials have different sample sizes (around 20 each), > > anova(dung.glm1, dung.glm2) > > is not applicable. has anyone an idea? > thanks in advance! > > ps: my advisor urges me to use the z-test (the common test statistic in my > field of research), but i reject that due to the small sample size. > > ______________________________________________ > 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. > -- Joris Meys Statistical Consultant Ghent University Faculty of Bioscience Engineering Department of Applied mathematics, biometrics and process control Coupure Links 653 B-9000 Gent tel : +32 9 264 59 87 joris.m...@ugent.be ------------------------------- Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php [[alternative HTML version deleted]] ______________________________________________ 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.
