Cheers,
Yingjie
On 19 Aug 2010, at 11:49, Gavin Simpson wrote:
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 data. Apparently, Quasi likelihood is at least a
better choice. I've noticed the hurdle they used for the paper comes
from package 'stats' instead of 'pscl', I didn't find this version of
hurdle in r...
Quasi-likelihood isn't solving the "over-dispersion comes from positive
part". It is a means of fitting models, just like maximum likelihood
etc. It will be the authors model that does the accounting for over
dispersion. They solve the parameters of this model using
quasi-likelihood.
Your claim about hurdle in stats is incorrect:
no object named ?hurdle? was found
no object named ?hurdle? was found
So they must be using something else. Here's a thought; why not give us
the reference/citation for the paper you are reading --- it is difficult
to speculate further without more details like the actual paper?
Hurdle models fit a point mass at zero, whilst the count part of the
model is truncated to not allow any further zeros be produced from it.
A zeroinflated (zeroinfl() in pscl) model fits a point mass at zero and
has an untruncated count model which will allow extra zeros be produced.
In both cases a negative binomial model may be fitted to the count part,
which may be sufficient to cope with remaining overdispersion in the
count part of your model.
I think you would be better off thinking where the overdispersion is
coming from and choosing an appropriate means to model it. You are being
blinded by this talk of quasi-likelihoods. There may well be a way of
fitting the model you want in R without resorting to quasi-likelihood
tricks. But as you haven't told us what model you want to fit or a
citation for the paper you want to replicate, there isn't much further
we can do.
HTH
G
On 19 Aug 2010, at 10:55, Gavin Simpson wrote:
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 my inbox...
Why would you want a quasi-likelihood when you could have the real
thing? Seriously, if there is no likelihood you can't do likelihood
ratio tests, compare models using AIC/BIC etc.
Just use hurdle() if it fits the form of model you are after. don't
worry about likelihoods, quasi or otherwise. Check you are happy with
the range of models you could fit with hurdle() and use it. If you
aren't happy then you'd need to look elsewhere, but don't get hung up on
the quasi-likelihood bit.
My Tuppence,
G
On 19 Aug 2010, at 10:17, Gavin Simpson wrote:
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
help file from hurdle in R, which said differently that"for non-zero
obs normally a truncated poisson/NB is used" ... just want to make
sure, does it really estimated the parameter by "quasi-likelihood"
Thanks,
Yingjie Zhang
Biostatistician
The authors of that paper might have fitted their hurdle model using a
quasi likelihood but that is not, AFAICT, what is used in the hurdle()
function in package 'pscl', which maximises a proper log likelihood.
But hard to say from what you have provided.
G
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Dr. Gavin Simpson [t] +44 (0)20 7679 0522
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