On Aug 2, 2012, at 21:09 , Bird_Girl wrote:
> Hi,
>
> I have a question regarding whether it is possible to do post hoc tests on a
> model fit with GAM {mgcv}. My response variable is abundance (no.
> individuals per plot), and I have one continuous predictor (light) and one
> factor (height) which includes 7 levels.
>
>> mod2=gam(log_abundance~s(light)+height+te(light,by=height)+s(long)+s(lat))
>
> The relationship between log_abundance and light at the seven levels of
> height all differ significantly from the overall relationship between
> log_abundance and light, and relationships at most of the 7 levels are not
> linear. I would like to do some kind of multiple comparison or post hoc
> test to determine whether the relationship between log_abundance and light
> differs significantly among the different levels of height (i.e., is the
> relationship at 200 m different from that at 400 m)? Is there any way to do
> this?
>
> Thanks in advance, and I apologize if this is a stupid question – I am new
> to R.
Nothing stupid about it, but maybe difficult to give a complete answer to in
email (a simplified, reproducible example would help so that readers can try
out suggestions).
I would expect that library(multcomp) is your friend. It should work since gam
objects have coef() and vcov() methods. Look into glht() and
mcp(height="Tukey").
--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd....@cbs.dk Priv: pda...@gmail.com
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