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]
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