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.