[R] Post-hoc tests in MASS using glm.nb
bryony
bryony at hawkconservancy.org
Mon May 16 19:46:07 CEST 2011
I am struggling to generate p values for comparisons of levels (post-hoc
tests) in a glm with a negative binomial distribution
I am trying to compare cell counts on different days as grown on different
media (e.g. types of cryogel) so I have 2 explanatory variables (Day and
Cryogel), which are both factors, and an over-dispersed count variable
(number of cells) as the response. I know that both variables are
significant, and that there is a significant interaction between them.
However, I seem unable to generate multiple comparisons between the days and
cryogels.
So my model is
Model1<-glm.nb(Cells~Cryogel+Day+Day:Cryogel)
The output gives me comparisons between levels of the factors relative to a
reference level (Day 0 and Cryogel 1) as follows:
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.2040 0.2743 4.389 1.14e-05 ***
Day14 3.3226 0.3440 9.658 < 2e-16 ***
Day28 3.3546 0.3440 9.752 < 2e-16 ***
Day7 3.3638 0.3440 9.779 < 2e-16 ***
Cryogel2 0.7097 0.3655 1.942 0.05215 .
Cryogel3 0.7259 0.3651 1.988 0.04677 *
Cryogel4 1.4191 0.3539 4.010 6.07e-05 ***
Day14:Cryogel2 -0.7910 0.4689 -1.687 0.09162 .
Day28:Cryogel2 -0.5272 0.4685 -1.125 0.26053
Day7:Cryogel2 -1.1794 0.4694 -2.512 0.01199 *
Day14:Cryogel3 -1.0833 0.4691 -2.309 0.02092 *
Day28:Cryogel3 0.1735 0.4733 0.367 0.71395
Day7:Cryogel3 -1.0907 0.4690 -2.326 0.02003 *
Day14:Cryogel4 -1.2834 0.4655 -2.757 0.00583 **
Day28:Cryogel4 -0.6300 0.4591 -1.372 0.16997
Day7:Cryogel4 -1.3436 0.4596 -2.923 0.00347 **
HOWEVER I want ALL the comparisons e.g. Cryogel 2 versus 4, 3 versus 2 etc
on each of the days. I realise that such multiple comparsions need to be
approached with care to avoid Type 1 error, however it is easy to do this in
other programmes (e.g. SPSS, Genstat) and I'm frustrated that it appears to
be difficult in R. I have tried the glht (multcomp) function but it gives me
the same results. I assume that there is some way of entering the data
differently so as to tell R to use a different reference level each time and
re-run the analysis for each level, but don't know what this is.
Please help!
Many thanks for your input
Bryony
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