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R: st: change the format of the pseudo log-likelihood in gb2fit
From
"Lucia R.Latino" <[email protected]>
To
<[email protected]>
Subject
R: st: change the format of the pseudo log-likelihood in gb2fit
Date
Fri, 6 Jul 2012 19:59:38 +0200
Dear Nick,
thanks for your hint.
I am trying to compare different nested distributions and I would love to
compare the log likelihood. With the scientific notation I cannot see any
difference. That is the reason why I was trying to obtain the full log
likelihood.
Combine Michael's code and -display- I am able to get the full log
likelihood. But I haven't been able to change how stata display the result
after gb2fit or similar command.
Does anyone can help me in writing the explicit expression for
log-likelihood for lognfit, dagumfit, smfit, fisk and Beta2 similar to what
Michal wrote for the gb2fit?
Thanks,
Lucia
-----Messaggio originale-----
Da: [email protected]
[mailto:[email protected]] Per conto di Nick Cox
Inviato: venerdì 6 luglio 2012 11:58
A: [email protected]
Oggetto: Re: st: change the format of the pseudo log-likelihood in gb2fit
-gb2fit- is a user-written program from SSC written by Stephen Jenkins.
-display- takes a format which can be used to change how a result is
displayed. Look at the help for -display- and for -format-. However, it is
difficult to see that further decimal places could be of either use or
interest.
Nick
On Fri, Jul 6, 2012 at 10:41 AM, Lucia R.Latino
<[email protected]> wrote:
> Dear Michal and Stephen,
>
> Thanks for your answer. I tried Michal's code and it actually recovers
> the log pseudolikelihood, however stata shows the value using again
> the scientific notation. Do you have any suggestion?
> Furthermore, I am not sure I am able to write the same code for the
> other distributions I am using (lognfit, smfit, dagumfit, fisk,
> beta2). Do you think there is a way to simply change the format of the
> Iteration so to have more precision?
>
> If it can helps I copied the result I obtained.
>
> Thanks,
> Lucia
>
> -----------------------------------------------------------start
> output
> ----------------------------------------------------------------------
> ------
> ------------
> gb2fit dec_ae, cdf(gb2cdf) pdf(gb2pdf) svy
>
> initial: log pseudolikelihood = -<inf> (could not be evaluated)
> feasible: log pseudolikelihood = -1.469e+08
> rescale: log pseudolikelihood = -1.469e+08
> rescale eq: log pseudolikelihood = -1.075e+08
> Iteration 0: log pseudolikelihood = -1.075e+08 (not concave)
> Iteration 1: log pseudolikelihood = -1.064e+08 (not concave)
> Iteration 2: log pseudolikelihood = -1.063e+08
> Iteration 3: log pseudolikelihood = -1.060e+08 (not concave)
> Iteration 4: log pseudolikelihood = -1.060e+08
> Iteration 5: log pseudolikelihood = -1.059e+08
> Iteration 6: log pseudolikelihood = -1.059e+08
> Iteration 7: log pseudolikelihood = -1.059e+08
> Iteration 8: log pseudolikelihood = -1.059e+08
> Iteration 9: log pseudolikelihood = -1.059e+08
> Iteration 10: log pseudolikelihood = -1.059e+08 Iteration 11: log
> pseudolikelihood = -1.059e+08 Iteration 12: log pseudolikelihood =
> -1.059e+08
>
> ML fit of GB2 distribution
>
> pweight: iwght Number of obs =
> 11183
> Strata: <one> Number of strata =
> 30
> PSU: <observations> Number of PSUs =
> 564
> Population size =
> 12272397
> F( 0, 535) =
> .
> Prob > F =
> .
>
> ----------------------------------------------------------------------
> ------
> --
> dec_ae | Coef. Std. Err. t P>|t| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------
> -------------+------
> --
> a |
> _cons | 2.327935 .2782045 8.37 0.000 1.781426
> 2.874444
> -------------+--------------------------------------------------------
> -------------+------
> --
> b |
> _cons | 3997.361 250.2252 15.98 0.000 3505.814
> 4488.907
> -------------+--------------------------------------------------------
> -------------+------
> --
> p |
> _cons | 1.603659 .2993526 5.36 0.000 1.015606
> 2.191712
> -------------+--------------------------------------------------------
> -------------+------
> --
> q |
> _cons | 2.983855 .6731986 4.43 0.000 1.661412
> 4.306297
> ----------------------------------------------------------------------
> ------
> --
>
> tempvar ll
> local wv= "`e(wvar)'"
>
> gen `ll' = lngamma(e(bp)+e(bq)) + log(e(ba)) + (e(ba)*e(bp)-1)*
> log(dec_ae) ///
> - e(ba)*e(bp)*log(e(bb)) - lngamma(e(bp)) - lngamma(e(bq)) - ///
> (e(bp)+e(bq))*log(1+(dec_ae/e(bb))^e(ba))
>
> qui sum `ll' [aw=`wv'] if e(sample),meanonly di r(sum)
>
> -1.059e+08
> -----------------------------------------------------------end output
> ----------------------------------------------------------------------
> ------
> ------------
>
> Lucia
>
>
> -----Messaggio originale-----
> Da: [email protected]
> [mailto:[email protected]] Per conto di Michal
> Brzezinski
> Inviato: giovedì 5 luglio 2012 23:15
> A: [email protected]
> Oggetto: Re: st: change the format of the pseudo log-likelihood in
> gb2fit
>
> You can recover pseudo log-likelihood writing an explicit expression
> for log-likelihood for a given model.
> For example, in case of GB2 model you could try the following code:
>
> gb2fit dec, cdf(gb2cdf) pdf(gb2pdf) svy tempvar ll local wv=
> "`e(wvar)'"
>
> gen `ll' = lngamma(e(bp)+e(bq)) + log(e(ba)) + (e(ba)*e(bp)-1)* log(dec)
///
> - e(ba)*e(bp)*log(e(bb)) - lngamma(e(bp)) - lngamma(e(bq)) - ///
> (e(bp)+e(bq))*log(1+(dec/e(bb))^e(ba))
> qui sum `ll' [aw=`wv'] if e(sample),meanonly di r(sum)
>
> ----------
> Hope this helps,
> Michal
>
> 2012/7/5 Lucia Latino <[email protected]>:
>> Dear Statalist,
>>
>> After running - gb2fit - smfit - lognfit - Stata reports the pseudo
>> log-likelihood with the scientific notation, but I want to view the
>> full likelihood.
>> Usually, I can obtain the full log-likelihood by using - display
>> e(ll)
>> -
>>
>> However, I need to add the option svy for the estimation (e.g. -
>> gb2fit dec, stats cdf(gb2cdf) pdf(gb2pdf) svy - ) and after adding
>> the option 'svy', Stata doesn't store anymore the pseudo
>> log-likelihood in
> e(ll).
>>
>> Is there any way to do change the format of the pseudo log-likelihood
>> in the iterations or a way to obtain it after the estimation?
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