Hi Chris,
There are many methods (Boyce index, maxKappa, etc.) to evaluated the
predictions of a model when applied to test dataset. More information is
given in the paper of Hirzel et al 2006 Ecological Modelling.
Furthermore, have a look at the package PresenceAbsence
Dear list,
I was wondering if there is a possibility to do model selection with the
method adonis of the package Vegan. If so, could any one explain me
about it or give me a hint were to look?
Kind regards,
Maarten
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On 5/08/10 09:51 AM, Maarten de Groot maarten.degr...@nib.si wrote:
Dear list,
I was wondering if there is a possibility to do model selection with the
method adonis of the package Vegan. If so, could any one explain me
about it or give me a hint were to look?
Maarten,
Not automatically:
I believe the recent discussion about AIC or p-values has missed a
crucial practical issue.
The AIC statistic reported by a default call to lmer() has NOTHING to do
with the choice of fixed effects. lmer() uses reml to define the fit of
a model. REML (residual ML or restricted ML) is a log
Hi Philip,
Thanks very much for this, i was completely unaware. I have read various
papers using lmer to calculate the AIC statistic and none have mentioned this?
I have just run through a random section of my models with this correction,
however the AIC / BIC values are the same with the
Chris Mcowen chrismco...@... writes:
Hi Philip,
Thanks very much for this, i was completely unaware. I have read various
papers using lmer to calculate the
AIC statistic and none have mentioned this?
I have just run through a random section of my models with this correction,
however
They are described as
“nearly” interchangeable because the ‘REML’ argument only applies
to calls to ‘lmer’ and the ‘nAGQ’ argument only applies to calls
to ‘glmer’
I am using lmer?
Thanks
Chris
On 5 Aug 2010, at 14:16, Manuel Morales wrote:
REML does not apply for glmer fits:
Chris/Ben
The lack of effect of the REML parameter is simply explained by the fact you
are fitting a binomial model. This causes the lmer call to default to a glmer
call in which the REML parameter is ignored. I also note that you are
specifying order/family in the random term, which I
In this case where a family is completely contained within an order, I think
that once the variance at family level has been fit there is no remaining
variance left to explain at the order level. Thus you should get the same
values for the fixed effects with both model specifications. Where a
Hi Andy
I don't think you're right, although there are two different questions. One is
whether introducing a higher level grouping where the structure is nested
allows any variance to be defined at that level. It obviously depends on the
data, but by and large the answer is yes, because the
My apologies to the list. My e-mail this morning about interpreting AIC
in lmer() was off-target. I did not notice that Chris's call to lmer()
included family = binomial, which means the actual estimation is done by
glmer(). glmer() ignores the REML= argument. glmer() only does
approximate
Better to be safe than sorry.
And I for one am glad u mentioned it. It's they type of knowledge one
might not pick up when using R to fit a mixed model for the first time.
It's certainly something I'll be keeping in mind should I ever use R for
mixed models!!!
Chris Howden
Founding Partner
Hi Chris,
I often do the following:
·Look at MSE
·Plot the residuals
·Plot predicted vs. actual (should be approximately linear)
o (very similar to the residual plot, but clients seem to understand it
better)
·Look at the min and max residuals, and think
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