Joana and any others: You cannot obtain a valid or useful measure of
relative importance of predictor variables across multiple models by
applying relative AIC weights or using model averaged coefficients unless
all your models included a single predictor (which, of course, is not what
is usually
Dear list
I'm using hurdle models to model fish count data versus habitat variables.
Because I got too many interpretable models (based on Akaike weights) I
would like to do model averaging and estimate the relative importance
(weight) of each habitat variables in each component of the hurdle (
Yaiza,
The problem is that two coefficients are perfectly correlated with each other.
Since this happens in the models with interactions but not additive effects, my
hunch is that the cross-product variable is very highly correlated with one of
the original variables. This can happen if one v
For the question 'if hurdle model is necessary', when you fit hurdle manually,
you will get two parameters, one from the count part, the other from 0 part. A
test of hypothesis parameter1=parameter2 then tests whether the hurdle is
needed or not.
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Renke Lühken wrote:
> The habitats are replicated, sorry for the confusion!
>
> How can I decide if I really need a zero-inflated model or is it just
> expert judgment?
> I decided that I need it, because more than 50% of the data are true
> zeros (detectability=100%) and therfore e.g. Martin et al
The habitats are replicated, sorry for the confusion!
How can I decide if I really need a zero-inflated model or is it just
expert judgment?
I decided that I need it, because more than 50% of the data are true
zeros (detectability=100%) and therfore e.g. Martin et al. (2005) or
Zuur et al., e
A few comments:
are the habitats replicated? If not, you have a fairly serious
experimental design problem -- you can't statistically distinguish
between the measured covariates and other, unmeasured/unintentional
differences among the habitats ...
* are you willing to treat complexity as a
Hi all,
I want to analyse an experiment at which insects were allowed to choose
between four habitats with different characteristics (see below). Number
of individuals per habitat were resampled six times (every 5 min). I
want to know which variables and which interactions of the variables
ha
on, Oregon
http://profile.usgs.gov/dhewitt
> Date: Thu, 19 Aug 2010 12:54:01 +0100
> From: Gavin Simpson
> To: Yingjie Zhang
> Cc: r-sig-ecology@r-project.org
> Subject: Re: [R-sig-eco] hurdle model
>
> On Thu, 2010-08-19 at 13:20 +0200, Yingjie Zhang wrote:
>>
Yingjie Zhang wrote:
> Dear all,
>
> Thanks for all your perspectives, I agree with Dr. Gavin Simpson's opinion
> that the author cooked the model by themselves, and there is no Hurdle
> function in package 'stats'.
>
> I got a data set of the abundance of microbial community, I think some of yo
Dear all,
Thanks for all your perspectives, I agree with Dr. Gavin Simpson's opinion that
the author cooked the model by themselves, and there is no Hurdle function in
package 'stats'.
I got a data set of the abundance of microbial community, I think some of you
will know how it looks like, i
Dear All,
I had a quick look at the internal functions used by pscl::hurdle to
do the numerical optimization by optim. It clearly corresponds to the
hurdle model defined in the paper/vignette, where the zero component
is based on a right censored random variable, that is 0 if the
original count da
On Thu, 2010-08-19 at 14:54 +0300, Gavin Simpson wrote:
> On Thu, 2010-08-19 at 13:20 +0200, Yingjie Zhang wrote:
> They fit several models and compare them:
>
> I. Poisson
> II. Negative Binomial
>III. Quasi-likelihood
> IV. Hurdle model
> V. zero-inflated model
>
> III sh
On Thu, 2010-08-19 at 13:20 +0200, Yingjie Zhang wrote:
> Thanks for the details, the paper is 'Comparing species abundance
> models' by Joanne M.Potts, Jane Elith. Click the link... on page 158,
> in the table, they compare 5 models, both Quasi-likelihood and Hurdle
> are mentioned.
>
> http://w
Thanks for the details, the paper is 'Comparing species abundance models' by
Joanne M.Potts, Jane Elith. Click the link... on page 158, in the table, they
compare 5 models, both Quasi-likelihood and Hurdle are mentioned.
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VBS-4KD5C2N-1&_
On Thu, 2010-08-19 at 11:14 +0200, Yingjie Zhang wrote:
> Hi,
>
> There is a reason why am I addict to Quasi likelihood, since Hurdle
> from 'pscl' use Zero Truncated Poisson regression for the non-zero
> part, which incapable of handling the over-disperson comes from the
> positive part of the da
Hi,
There is a reason why am I addict to Quasi likelihood, since Hurdle from
'pscl' use Zero Truncated Poisson regression for the non-zero part, which
incapable of handling the over-disperson comes from the positive part of the
data. Apparently, Quasi likelihood is at least a better choice. I
On Thu, 2010-08-19 at 10:30 +0200, Yingjie Zhang wrote:
> I'd like to try the same way to my dataset, hurdle but estimated by
> 'quasi-likelihood', but it's not in the standard 'pscl' package I
> think, right?
Please keep discussion on list; just because I replied doesn't give you
a direct line to
On Thu, 2010-08-19 at 09:52 +0200, Yingjie Zhang wrote:
> Hello everyone,
>
> Does anyone of you using hurdle model? I am reading a paper which said
> " Hurdle model removes effect of zero-inflation and over-dispersion in
> the non-zero observations using a quasi-likelihood", I've checked the
> he
Hello everyone,
Does anyone of you using hurdle model? I am reading a paper which said " Hurdle
model removes effect of zero-inflation and over-dispersion in the non-zero
observations using a quasi-likelihood", I've checked the help file from hurdle
in R, which said differently that"for non-zer
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