0.00 1447.68
37 140.00 1405.89
38 430.00 393.62
39 410.00 461.28
40 410.00 290.35
41 14 1275.61 5695.19
42 12 60.61 3502.27
43 11 129.36 2962.34
44 13 19.95 3786.72
45 110.00 1303.16
46 130.00 1044.52
47 120.00 461.22
48 140.00 363.79
--On 13 March 2009 11:01 +0100 ONKELINX, Thierry
thierry.onkel...@inbo.be wrote:
Dear Emma,
Have you tried a simpler model? False convergence can be due to an
overcomplex model. Can you give a brief outline of your data? E.g. how
many sites, how many data per site, ... Cross tabulations of all pairs
of factor variables are usefull too.
HTH,
Thierry
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
thierry.onkel...@inbo.be
www.inbo.be
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
-Oorspronkelijk bericht-
Van: Emma Stone [mailto:emma.st...@bristol.ac.uk]
Verzonden: vrijdag 13 maart 2009 10:50
Aan: ONKELINX, Thierry; Emma Stone; r-help@r-project.org
Onderwerp: RE: [R] Mixed models fixed effects
Hi Thierry,
That's great thanks!
I have done as you have said but I keep getting a warning message here
is
my code:
G1Hvol-glmer(passes~hvolume+style+habitat(1|Site),family = poisson)
And this is the message i get:
Warning message:
In mer_finalize(ans) : false convergence (8)
any ideas??
Emma
--On 11 March 2009 15:45 +0100 ONKELINX, Thierry
thierry.onkel...@inbo.be wrote:
Hi Emma,
Continuous predictors are no problem at all. You can mix both
continuous
and categorial predictors if needed. I suppose your response are
counts
(the number of bats that passes)? In that case a generalised linear
mixed model is more appropriate. With the lme4 package you could try
something like this:
library(lme4)
Model - glmer(BatPasses ~ Width + Height + (1|Site), family =
poisson)
HTH,
Thierry
PS There is a mailing list dedicated to mixed models:
R-Sig-MixedModels
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
thierry.onkel...@inbo.be
www.inbo.be
To call in the statistician after the experiment is done may be no
more
than asking him to perform a post-mortem examination: he may be able
to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does
not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
-Oorspronkelijk bericht-
Van: r-help-boun...@r-project.org
[mailto:r-help-boun...@r-project.org]
Namens Emma Stone
Verzonden: woensdag 11 maart 2009 15:29
Aan: r-help@r-project.org
Onderwerp: Re: [R] Mixed models fixed effects
Dear All,
This may sound like a dumb question but I am trying to use a mixed
model
to
determine the predictors of bat activity along hedges within 8 sites.
So
my
response is continuous (bat passes) my predictors fixed effects are
continuous (height metres), width (metres) etc and the random effect
is
site - can you tell me if the fixed effects can be continuous as all
the
examples I have read show them as categorical, but this is not covered
in
any documents I can find.
Help!
Emma
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bevestigd is door een geldig ondertekend document. The views expressed
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be
regarded as stating an official position of INBO, as long as the
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is not confirmed by a duly signed document.
--
Emma Stone
Postgraduate Researcher
Bat Ecology
