I forgot to add that yes, I've done my homework, and that it seems to me that answers pointing to zero-inflated Poisson (and negative binomial) are irrelevant ; I do not have a mixture of distributions but only part of one distribution, or, if you'll have it, a "zero-deflated Poisson".
An answer by Brian Ripley (http://finzi.psych.upenn.edu/R/Rhelp02/archive/11029.html) to a similar question leaves me a bit dismayed : if it is easy to compute the probability function of this zero-deflated RV (off the top of my head, Pr(X=x)=e^-lambda.lambda^x/(x!.(1-e^-lambda))), and if I think that I'm (still) able to use optim to ML-estimate lambda, using this to (correctly) model my problem set and test it amounts to re-writing some (large) part of glm. Furthermore, I'd be a bit embarrassed to test it cleanly (i. e. justifiably) : out of the top of my head, only the likelihood ration test seems readily applicable to my problem. Testing contrasts in my covariates ... hum ! So if someone has written something to that effect, I'd be awfully glad to use it. A not-so-cursory look at the existing packages did not ring any bells to my (admittedly untrained) ears... Of course, I could also bootstrap the damn thing and study the distribution of my contrasts. I'd still been hard pressed to formally test hypotheses on factors... Any ideas ? Emmanuel Charpentier Le samedi 18 avril 2009 à 19:28 +0200, Emmanuel Charpentier a écrit : > Dear list, > > I have the following problem : I want to model a series of observations > of a given hospital activity on various days under various conditions. > among my "outcomes" (dependent variables) is the number of patients for > which a certain procedure is done. The problem is that, when no relevant > patient is hospitalized on said day, there is no observation (for which > the "number of patients" item would be 0). > > My goal is to model this number of patients as a function of the > "various conditions" described by my independant variables, mosty of > them observed but uncontrolled, some of them unobservable (random > effects). I am tempted to model them along the lines of : > > glm(NoP~X+Y+..., data=MyData, family=poisson(link=log)) > > or (accounting for some random effects) : > > lmer(NoP~X+Y....+(X|Center)), data=Mydata, family=poisson(link=log)) > > While the preliminary analysis suggest that (the right part of) a > Poisson distribution might be reasonable for all real observations, the > lack of observations with count==0 bothers me. > > Is there a way to cajole glm (and lmer, by the way) into modelling these > data to an "incomplete Poisson" model, i. e. with unobserved "0" > values ? > > Sincerely, > > Emmanuel Charpentier > > ______________________________________________ > 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. > ______________________________________________ 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.