Dear David,
You have missed the fact that exp(-a) = 1/exp(a). Additive effects on the
log scale are multiplicative effects on the original scale.
Best regards,
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
2015-05-15 0:53 GMT+02:00 David Robichaud <drobich...@lgl.com>:
> Hello,
>
> I am trying to do a BACI analysis on count data, and I am having trouble
> interpreting the output from multcomp::glht. I don't understand how the
> contrast's coefficients are related to effect size (if at all??).
>
> I have 5 treatment conditions (one is a control), and I have counts from
> before the treatments were applied and after. Let's say that my model form
> is this ("Period" is the 'before' vs 'after' factor):
>
> m.pois <- glm(Y_count ~ Treatment + Period + Treatment:Period,
> data = df.temp,
> family = "poisson")
>
> As in all BACI designs, I am interested in the interaction term, i.e., the
> differences of the differences. For example, I'd like to test whether
> TreatmentVR30 changed more than the Control did:
> (TreatmentVR30Later - TreatmentVR30Before) - (ControlLater -
> ControlBefore).
>
> I have done the math, and I created all my planned contrasts, run them
> through the multcomp::glht, and I am struggling to interpret the output.
> As an example of my confusion, I ran the same contrast in two directions
> (A-B and B-A), which should give the same result (one positive, one
> negative):
>
> contr <- rbind(
> "VR30 vs Control" = c(0, 0, 0, 0, 0, 0, -1, 0, 0, 1),
> "Control vs VR30" = c(0, 0, 0, 0, 0, 0, 1, 0, 0, -1) )
> m.pois.contr <- summary(glht(m.pois, contr))
>
> which works perfectly, returning one positive and one negative estimate,
> as expected:
>
> Linear Hypotheses:
> Estimate Std. Error z value Pr(>|z|)
> VR30 vs Control == 0 0.7354 0.5621 1.308 0.191
> Control vs VR30 == 0 -0.7354 0.5621 -1.308 0.191
> (Adjusted p values reported -- single-step method)
>
> Understanding that the estimates are in log space (due to the link
> function of the poisson family in the glm), I back transformed using
> exp(coef(m.pois.contr) to get:
>
> VR30 vs Control Control vs VR30
> 2.0862414 0.4793309
>
> So, which is it? Did Control change more than VR30, or did VR30 change
> more than Control, and for both questions, by how much?
>
> Clearly I am missing something here. I expect that this will be a simple
> fix, but surprisingly, I cannot find it anywhere online.
>
> Thanks in advance to anyone who can help,
>
> David Robichaud, Victoria, BC, Canada
>
> ______________________________________________
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>
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