Re: [R] Multilevel modeling with count variables

Thanks everyone for the helpful ideas. It appears that this will be more difficult than I thought. I don't necessary have an inclination toward p-values, but many journals certainly do. I would be willing to try to calculate the confidence intervals around the estimates, but I haven't gotten an

Re: [R] Multilevel modeling with count variables

Your request might find better answers on the R-SIG-mixed-models list ... Anyway, some quick thoughts : Le vendredi 26 mars 2010 à 15:20 -0800, dadrivr a écrit : > By the way, my concern with lmer and glmer is that they don't produce > p-values, The argumentation of D. Bates is convincing ... A

Re: [R] Multilevel modeling with count variables

Whoops, sorry that's pt(), not dt() Thanks Dennis! - Corey Sparks, PhD Assistant Professor Department of Demography and Organization Studies University of Texas at San Antonio 501 West Durango Blvd Monterey Building 2.270C San Antonio, TX 78207 210-458-3166 corey.sparks 'at' utsa.edu https://

Re: [R] Multilevel modeling with count variables

have you tried using glmer? If your dependent variable is poisson distributed, you can try something like fit<-glmer(y~x+(1|group), family=poisson) and if you have differential exposure, you can do fit<-glmer(y~offset(log(exposure))+x+(1|group), family=poisson) Is this what you are asking? With

Re: [R] Multilevel modeling with count variables

By the way, my concern with lmer and glmer is that they don't produce p-values, and the techniques used to approximate the p-values with those functions (pvals.fnc, HPDinterval, mcmcsamp, etc.) only apply to Gaussian distributions. Given that I would likely be working with quasi-poisson distribut