Re: [R] glht with a glm using a Gamma distribution

From: Charles C. Berry <cberry_at_tajo.ucsd.edu>
Date: Tue, 15 Apr 2008 10:14:35 -0700

On Mon, 14 Apr 2008, Jarrett Byrnes 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.
>
> However, when I use glht to look for differences between groups, I get
> significant differences where there are none. Now, I'm all for
> eyeballing means +/- 95% CIs. However, I've had reviewers and
> committee members all tell me that I needed them. Oy. Here's the
> code and some of the sample data that, when visualized, is clearly not
> different in the comparisons I'm making, and, yet, glht (at least, how
> I'm using it, which might be improper) says that the differences are
> there.
>
> Hrm.
>
> I'm guessing I'm just using glht improperly, but, any help would be
> appreciated!

Good guess.

Compare this to below:

> coef(a.glm)-coef(a.glm)[1]

(Intercept) trtb trtc trtd

    0.000000 1.964595 1.678159 2.128366
>

You are testing the hypothesis that B - 2 * A == 0, etc.

HTH, Chuck

>
> 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)
>
>
> -Jarrett
>
>
>
>
> ----------------------------------------
> Jarrett Byrnes
> Population Biology Graduate Group, UC Davis
> Bodega Marine Lab
> 707-875-1969
> http://www-eve.ucdavis.edu/stachowicz/byrnes.shtml
>
>
> [[alternative HTML version deleted]]
>
>

Charles C. Berry                            (858) 534-2098
                                             Dept of Family/Preventive Medicine
E mailto:cberry_at_tajo.ucsd.edu	            UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901



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