Dieter Menne wrote:
> Rainer M Krug wrote:
>>> I have (8 measures * 96 groups) = 768 datasets for which I did linear
>>> regressions using lm().
Sorry for my (probably incorrect) usage of "group" and "measure".
Here a (probably better) description of my design:
I ran 96 different simulations (spread of a species along a transect)
over four generations, based on different parameter sets. These resulted
in 96 results (groups). Now I have 8 different ways to describe the
result of one simulation based on x individuals further away then
distance d, where x is replaced by 1, 5, 10, 50, 100, 500, 1000 and 5000
(measures). For each group / simulation, I did linear regressions for
each measure
> lm.measure_1 <- lm(distance ~ generation)
...
resulting in lm.measure_1 to lm.measure_8
Within each group (based on one simulation), I get a different slope for
each measure.
What I want to know is if these resulting slopes differ within one group
/ simulation.
>>>
>>> Now I want to compare the slopes for each of the 8 measures in each of
>>> the 96 groups. As I understand , I can not use
anova(lm1, ..., lm8)
>>> as the lm1 ... lm8 are based on different datasets.
>>>
>
> Instead of doing this in two steps, you would better use lme in package nlme
> to
> test the hypothesis in one run. I don't understand the details of you design,
> but is looks like the oxboys example in Pinheiro/Bates (the book of nlme)
> could
> give you a first starter.
>
> Dieter
>
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--
Rainer M. Krug, Dipl. Phys. (Germany), MSc Conservation
Biology (UCT)
Department of Conservation Ecology and Entomology
University of Stellenbosch
Matieland 7602
South Africa
Tel:+27 - (0)72 808 2975 (w)
Fax:+27 - (0)21 808 3304
Cell: +27 - (0)83 9479 042
email: [EMAIL PROTECTED]
[EMAIL PROTECTED]
__
R-help@stat.math.ethz.ch mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.