Re: st: question concerning translation from Matlab to Mata

[DC <dcase79@gmail.com>]

Hi Brian and Matthew,

Thanks a lot for the help. I tried those suggestions and unfortunately
I am still getting the following error:

 logitmle():  3301  subscript invalid

Thanks,
Marcus

On Thu, Mar 10, 2011 at 10:39 PM, Matthew J Baker
<matthew.baker@hunter.cuny.edu> wrote:
> Another problem that might produce the conformability error is the use of (y==0). I'm guessing that this might be better coded as (y:==0).
>
> MJB
>
> Dr. Matthew J. Baker
> Department of Economics
> Hunter College and the Graduate Center, CUNY
>
>
> ---- Original message ----
>>Date: Thu, 10 Mar 2011 18:09:53 -0500
>>From: owner-statalist@hsphsun2.harvard.edu (on behalf of "Brian P. Poi" <brian@poiholdings.com>)
>>Subject: Re: st: question concerning translation from Matlab to Mata
>>To: statalist@hsphsun2.harvard.edu
>>
>>
>>
>>On 3/10/2011 5:55 PM, DC wrote:
>>> Hi All,
>>>
>>> I'm attempting to translate some code for an optimal stopping problem
>>> that I had previously written in Matlab to Mata and I've encountered a
>>> problem
>>> in writing down a likelihood function.
>>>
>>> A simplified version in Matlab would be a multinomial logit with three
>>> choices, choice 0 parameter normalized to zero, :
>>> ...
>>> [n k]=size(x);
>>> D = 1+exp(x*b(1:k))+exp(x*b(k+1:2*k));
>>> p_0 = 1./D;
>>> p_1 = exp(x*b(1:k))./D;
>>> p_2 = exp(x*b(k+1:2*k))./D;
>>> lnlike=-sum( (y==0).*log(p_0) + (y==1).*log(p_1) +(y==2).*log(p_2));
>>> ....
>>> However is it not clear how to specify and estimate something like
>>> this using Mata
>>> I tried the following:
>>>
>>> void lmle(todo,b,y, x,lnf, S, H)
>>> {
>>>      k = cols(x)
>>>      b1 = b[1::k,1]
>>>      b2 = b[k+1::2*k,1]
>>>      xb1 = x*b1'
>>>      xb2 = x*b2'
>>>
>>>      d = 1 :+ (exp(xb1) + exp(xb1))
>>>      p_0 = 1 :/ d
>>>      p_1 = exp(xb1) :/ d
>>>      p_2 = exp(xb2) :/ d
>>>      lnf = (y==0) :* log(p_0) + (y==1) :* log(p_1)  + (y==2) :* log(p_2)
>>> }
>>>
>>> S = optimize_init()
>>> optimize_init_evaluator(S,&lmle())
>>> optimize_init_evaluatortype(S, "gf0")
>>> optimize_init_argument(S,1,y)
>>> optimize_init_argument(S,2,x)
>>> optimize_init_params(S, J(1,cols(x),0))
>>>
>>> b = optimize(S)
>>>
>>
>>
>>One immediate problem is that -optimize- passes the parameters as a row
>>vector, not a column vector, so the lines
>>
>> >     b1 = b[1::k,1]
>> >     b2 = b[k+1::2*k,1]
>>
>>should probably be
>>
>>       b1 = b[1, 1::k]
>>       b2 = b[1, k+1::2*k]
>>
>>That may or may not be the only problem.
>>
>>    -- Brian Poi
>>    -- brian@poiholdings.com
>>
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>



-- 
Marcus Casey, Ph.D.
Duke University

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