-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Can you please send this to r-sig-mixed-models at r-project.org ? I think the answer would be of general interest. I do have one paper (Pasch et al Am Nat 2013 in which we initially considered using a mixed model but decided not to in the end -- you could cite that in support ...
On 15-03-05 11:05 AM, Boris Fuchs wrote:
Hi Ben
Thirst: thanks for all the answers you share in the net.
Guess you are busy. I try to be short.
I try to publish my master thesis:
I have a data set of 106 Scandinavian wolves. A binary response
whether they are tested positive or negative for a parasite (Sarcoptic
mange). I test if individual variables (sex, age?) or environmental
variables (fox density, latitude?) are better explaining the test
results (positive or negative).
I use glm?s, selected by AICc in R.
16 are positive.
I am aware of potential overfitting problems (max K = n/10 or better
20?)
About 40 individuals got sampled >1 up to 4 times. ?> random factor
Some territories are sampled multiple times. ? random factor
However:
I did not succeed with building random structures and I belief because
of missing variability within the random factors. Most of the
individuals are tested only once. Easier if most of them would be
tested several times and would be both, positive and negative.
The same with the territories. Is this right?
I would like to state the lacking variation as a reason to not use a
random structure. But I need some published support (not to say it
works anyway, but not). I seem not skilled enough to understand
(select) the key literature. Do you have any hint?
Since most of the individuals are sampled once and the time between
the sampling is by far enough to change from positive to negative and
vica versa I think I can take the risk of violation of independence.
Boris
boris fuchs ole evenstadsvei 1156 2480 koppang +47 41 51 49 30
boris.fuchs at schultraining.ch
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