From: Charles C. Berry <cberry_at_tajo.ucsd.edu>

Date: Tue, 15 Apr 2008 10:14:35 -0700

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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. Received on Tue 15 Apr 2008 - 17:38:07 GMT

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 Diegohttp://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901

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