To reply, in case anyone reads this, the problem was of course in the setup of the matrix, and there are two possible solutions.
The first, for a model with only a single set of groupings, is to use mcp. So, even with this contrast matrix contra<-rbind("A v. B" = c(-1,1,0,0), "A v. C" = c(-1,0,1,0), "A v. D" = c(-1,0,0,1)) The following will produce the desired analysis: summary(glht(a.glm, linfct=mcp(trt=contra))) If one wants to use the coefficients, however, one would need something like as follows contrb<-rbind("A v. B" = c(0,1,0,0), "A v. C" = c(0,0,1,0), "A v. D" = c(0,0,0,1)) Note, that coefficients are important in the case of factorial designs (e.g. if there was a block and block:trt effect included in the model). In this case, one needs to look at the coefficients and setup the appropriate contrast as on contr. At least, I think so. I have not yet found a way to use mcp for factorial designs. Does one exist? -Jarrett jebyrnes wrote: > > Quick question about the usage of glht. I'm working with a data set > from an experiment where the response is bounded at 0 whose variance > increases with the mean, and is continuous. A Gamma error > distribution with a log link seemed like the logical choice, and so > I've modeled it as such. > > I'm guessing I'm just using glht improperly, but, any help would be > appreciated! > > trt<-c("d", "b", "c", "a", "a", "d", "b", "c", "c", "d", "b", "a") > trt<-as.factor(trt) > > resp<-c(0.432368576, 0.265148862, 0.140761439, 0.218506998, > 0.105017007, 0.140137615, 0.205552589, 0.081970097, 0.24352179, > 0.158875904, 0.150195422, 0.187526698) > > #take a gander at the lack of differences > boxplot(resp ~ trt) > > #model it > a.glm<-glm(resp ~ trt, family=Gamma(link="log")) > > summary(a.glm) > > #set up the contrast matrix > contra<-rbind("A v. B" = c(-1,1,0,0), > "A v. C" = c(-1,0,1,0), > "A v. D" = c(-1,0,0,1)) > library(multcomp) > summary(glht(a.glm, linfct=contra)) > --- > Yields: > > Linear Hypotheses: > Estimate Std. Error z value p value > A v. B == 0 1.9646 0.6201 3.168 0.00314 ** > A v. C == 0 1.6782 0.6201 2.706 0.01545 * > A v. D == 0 2.1284 0.6201 3.433 0.00137 ** > --- > Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 > (Adjusted p values reported) > > > -- View this message in context: http://www.nabble.com/glht-with-a-glm-using-a-Gamma-distribution-tp16694729p16709118.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.