Dear Gabor, Arne, Ravi, R users,
I am firstly trying the maximum likelihood approach, then will try the
Bayesian approach.
The likelihood function, and the log likelihood function, will depend on
the pdf of the error e in the formula:
y=f(theta*x)+e
Now let's say that e is Gaussian distributed, then I can use LS which
is the same as ML in this case, and the residuals would be distributed
Gaussian. Is that right?
If e is distributed differently (for example: beta, in the continuous
case, or binomial, in the discrete case), then I am better off by using
Maximum Likelihood. How would the residual be distributed? Should they
not be distributed the same as e?
Best,
Gabor Grothendieck wrote:
For specific questions on the betareg package contact the maintainer.
If the likelihood based approaches are giving too much difficulty try
moving to a Bayesian framework (WinBUGS/R2WinBUGS, JAGS/r2jags, etc.)
On Wed, Mar 17, 2010 at 10:03 AM, Corrado <ct...@york.ac.uk> wrote:
Dear Arne, Gabor,
I solved the problem with betareg (downloaded the package). I run it on my
data, and unfortunately the constraint is definitively active, if I remove
the active variables, I then remove the most significant variables!
Of course the error is important, not the distribution of the variable.
In this case, one of the assumptions is that the error may be distributed ~
beta. I think that betareg makes this assumption, am I right?
I am finding it difficult to solve two problems:
1) write the maximum likelihood function (what do you suggest?)
2) deal with the fact that a few factors actually have values of y (the
response) at the extremes: that is 0 and 1. But that mean that the link
function returns Infinite values in that case ....
3) the error is dependent on E(y).
PS: Additional silly question: what is the discrete equivalent of beta?
binomial?
Arne Henningsen wrote:
On 17 March 2010 14:22, Gabor Grothendieck <ggrothendi...@gmail.com>
wrote:
Contact the maintainer regarding problems with the package. Not sure
if this is acceptable but if you get it to run you could consider just
dropping the variables from your model that correspond to active
constraints.
Also try the maxLik package. You will have to define the likelihood
yourself but it does support constraints.
Yes. And specifying the likelihood function is probably (depending on
your distributional assumptions) not too complicated.
BTW: Even if your y follows a beta distribution, it does not mean that
your error term also follows a beta distribution. And it the
distribution of the error term which is crucial for specifying the
likelihood function.
/Arne
--
Corrado Topi
PhD Researcher
Global Climate Change and Biodiversity
Area 18,Department of Biology
University of York, York, YO10 5YW, UK
Phone: + 44 (0) 1904 328645, E-mail: ct...@york.ac.uk
--
Corrado Topi
PhD Researcher
Global Climate Change and Biodiversity
Area 18,Department of Biology
University of York, York, YO10 5YW, UK
Phone: + 44 (0) 1904 328645, E-mail: ct...@york.ac.uk
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