but be careful how much you interpret this.
Regards
Mike
From: Fernando Schmidt [mailto:schmidt...@gmail.com]
Sent: 14 December 2011 11:40
To: Dunbar, Michael J.
Cc: r-sig-ecology@r-project.org
Subject: Re: [R-sig-eco] Possible bug with dredge function
Dear Mike,
Thanks for your help. To answer
Hi Bex
Did you mean the dredge function from the MuMIn package? I'm not really sure
what you mean by it does not take into account correlates or significance.
The point about dredge is that it is part of a family of functions for
multi-model inference which specifically avoid concepts of
Hi Rob
As it stands your model won't really do what you are expecting.
Start with a model like this:
fish.mod1-lmer(fishsize ~ pH + landuse + conductivity + (1 | subcatchment),
data=mydata)
You don't say whether you have multiple samples within each subcatchment + year
combination, or
Hi Rob
A couple of suggestions
1. You can use the glht command in the multcomp to get predictions for
different factor levels with simultaneous se's / ci's, you just need to
formulate the correct contrast(s)
2. You can make ad-hoc predictions by matrix-multiplying the parameter
estimates
Hi Tim
You haven't really explained where your group variable in the glmm has come
from. Moving from glm to glmm you've changed two things, adding the grouping
and the autocorrelation as well.
You have to be very careful when using the autocorrelation function. As it
stands the model will
, then the valley
splits to form two secondary tributaries 'vord' and 'hint'.
Given that my points become less well order toward high elevation should
I use form= ~1|group?
On 25.08.11 11:21, Dunbar, Michael J. wrote:
Hi Tim
You haven't really explained where your group variable in the glmm has
Edgar
I agree with what Chris says
(1 + BOAT | fYEAR) makes no sense
It is probably
(1|BOAT) + (1|fYEAR) that you want
Providing you have enough boats and years to estimate variance components for
these two factors: please feel free to post any further queries but you will
need to provide
Dear Cliff
Using a mixed-effects model (also variously termed multilevel or hierarchical
model) could well be what you are looking for. These are implemented in several
R packages.
In your study, in mixed model parlance, patient identifier would be a random
effect (you don't say how many
Have a look at the bio.infer package
Regards
Mike
-Original Message-
From: r-sig-ecology-boun...@r-project.org
[mailto:r-sig-ecology-boun...@r-project.org] On Behalf Of Yong Zhang
Sent: 14 March 2011 09:10
To: r-sig-ecology
Subject: [R-sig-eco] CALIBRATE program
Hi all,
Today, I
Hi Doug
Your first model can't be right, as AM/PM is clearly a fixed effect: it has
just two levels and you are only interested in making inferences about those
two levels. So you can't have AMPM on the right of the random part of the
model. Try not to think of nesting fixed effects in random
Dear Yong
The simplest approach would be to calculate a phi value for each stream, and
then apply conventional anova.
There is some background on phi here:
http://en.wikipedia.org/wiki/Particle_size_%28grain_size%29
There is an example of a simple algorithm to calculate phi from discrete
, is handled
automatically.
If you don't believe me then you may wish to ask on the r sig for mixed models.
Regards
Mike
-Original Message-
From: Hedberg Peter [mailto:phedb...@biol.uw.edu.pl]
Sent: 07 December 2010 19:00
To: Dunbar, Michael J.; r-sig-ecology@r-project.org
Subject: Sv: RE: [R
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