You could have a look at the VGAM (vector glm /gam models) at CRAN. Kjetil
On Tue, Jan 5, 2010 at 5:59 PM, Corey Sparks <corey.spa...@utsa.edu> wrote: > Dear R Users, > I'm working on a problem where I have a multivariate response vector of > counts and a continuous predictor. > I've thought about doing this the same way you would do a Multvariate > regression model with normally distributed data, but since these data are > counts, they are probably better modeled with a Poisson distribution. > > For example > y1<-rpois(100,3.5) > y2<-rpois(100,1.5) > y3<-rpois(100,.09) > x<-rnorm(100, mean=25, sd=10) > dat<-data.frame(y1, y2, y3, x) > > #Get the Multivariate linear model assuming normality > fit<-lm(cbind(y1,y2,y3)~x, data=dat) > fit.0<-update(fit, ~1) > #Calculate Pillai's trace for global model test > anova(fit, fit.0) > > But, if I try this approach with glm() instead of lm(), I get the error > indicating that a multivariate response vector isn't allowed in glm > > fit.pois<-glm(cbind(y1,y2,y3)~x, data=dat, family=poisson) > Error: (subscript) logical subscript too long > > If anyone has experience with a multivariate Poisson response vector I would > gladly appreciate any suggestions. > Corey Sparks > > -- > Corey Sparks > 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://rowdyspace.utsa.edu/users/ozd504/www/index.htm > > ______________________________________________ > 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.
