From: jebyrnes <jebyrnes_at_ucdavis.edu>

Date: Tue, 15 Apr 2008 13:50:31 -0700 (PDT)

Date: Tue, 15 Apr 2008 13:50:31 -0700 (PDT)

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)
**>
**>
**>
*

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