[R] Constrained non linear regression using ML

[Arne Henningsen]

On 17 March 2010 14:22, Gabor Grothendieck <ggrothendieck at 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

> On Wed, Mar 17, 2010 at 9:07 AM, Corrado <ct529 at york.ac.uk> wrote:
>> Dear Gabor,
>>
>> 1) The constraints are active, at least from a formal point view.
>> 3) I have tried several times to run betareg.fit on the data, and the only
>> thing I can obtain is the very strange error:
>>
>> Error in dimnames(x) <- dn :  length of 'dimnames' [2] not equal to array
>> extent
>>
>> The error is strange because, because the function dimnames is not called
>> anywhere.
>> Regards
>>
>> Gabor Grothendieck wrote:
>>>
>>> Try it anyways -- maybe none of your constraints are active.
>>>
>>> On Wed, Mar 17, 2010 at 6:01 AM, Corrado <ct529 at york.ac.uk> wrote:
>>>
>>>>
>>>> Dear Gabor, dear R users,
>>>>
>>>> I had already read the betareg documentation. As far as I can understand
>>>> from the help, it does not allow for constrained regression.
>>>>
>>>> Regards
>>>>
>>>>
>>>> Gabor Grothendieck wrote:
>>>>
>>>>>
>>>>> Check out the betareg package.
>>>>>
>>>>> On Tue, Mar 16, 2010 at 2:58 PM, Corrado <ct529 at york.ac.uk> wrote:
>>>>>
>>>>>
>>>>>>
>>>>>> Dear R users,
>>>>>>
>>>>>> I have to fit the non linear regression:
>>>>>>
>>>>>> y~1-exp(-(k0+k1*p1+k2*p2+ .... +kn*pn))
>>>>>>
>>>>>> where ki>=0 for each i in [1 .... n] and pi are on R+.
>>>>>>
>>>>>> I am using, at the moment, nls, but I would rather use a Maximum
>>>>>> Likelhood
>>>>>> based algorithm. The error is not necessarily normally distributed.
>>>>>>
>>>>>> y is approximately beta distributed, and the volume of data is medium
>>>>>> to
>>>>>> large (the y,pi may have ~ 40,000 elements).
>>>>>>
>>>>>> I have studied the packages in the task views Optimisation and Robust
>>>>>> Statistical Methods, but I did look like what I was looking for was
>>>>>> there.
>>>>>> Maybe I am wrong.
>>>>>>
>>>>>> The nearest thing was nlrob, but even that does not allow for
>>>>>> constraints,
>>>>>> as far as I can understand.
>>>>>>
>>>>>> Any suggestion?
>>>>>>
>>>>>> Regards
>>>>>>
>>>>>> --
>>>>>> 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: ct529 at york.ac.uk
>>>>>>
>>>>>> ______________________________________________
>>>>>> R-help at r-project.org mailing list
>>>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>>>> PLEASE do read the posting guide
>>>>>> http://www.R-project.org/posting-guide.html
>>>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>>>
>>>>>>
>>>>>>
>>>>
>>>> --
>>>> 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: ct529 at 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: ct529 at york.ac.uk
>>
>>
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>



-- 
Arne Henningsen
http://www.arne-henningsen.name