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Re: st: -ml- : how to save the likelihood contribution for each observation in a variable?


From   "Eva Poen" <eva.poen@gmail.com>
To   Statalist <statalist@hsphsun2.harvard.edu>
Subject   Re: st: -ml- : how to save the likelihood contribution for each observation in a variable?
Date   Fri, 10 Oct 2008 18:40:09 +0100

Ok, I finally got to the bottom of this problem. But I don't
understand it at all; I'd be grateful for explanations.

It appears that, as soon as there are missing values somewhere in the
variables of the model (even if these observations are excluded via
[if] or [in]), the likelihood will not be saved. The following
example, using the auto data, shows that in the first case (rep78
contains missing values) nothing is saved. In the second case (no
missing values) it works! In the third case (one observation with all
missings added but then excluded from the sample) it doesn't work once
again.

The -trace- reveals that during iterations, the values are saved in
all three cases. I was unable to find the piece of internal code that
reverts everything back in the case of missings.

This doesn't make sense to me at all. Any ideas?

Many thanks,
Eva

(The example is in d0 only because my own program is d0. I also paste
the output, for comparison.)

*************************************
capture program drop weird_d0
program weird_d0
    version 9.2
    args todo b lnf
    tempvar xb lnj
    tempname lns sigma
    mleval `xb'    = `b', eq(1)
    mleval `lns'   = `b', eq(2) scalar
    scalar `sigma'     = exp(`lns')

    // dependent variable:
    local y $ML_y1

    quietly {

        /* standard regression model */
        gen double `lnj' = log(normalden(`y',`xb',`sigma')) if $ML_samp==1
        replace  logLL   = `lnj' if $ML_samp==1

        mlsum `lnf' = `lnj' if $ML_samp==1
    }
end


sysuse auto, clear

gen logLL = .

// First case: missing values in one variable (rep78)
ml model d0 weird_d0 (Model:  price = rep78 foreign weight ) /lnsigma
ml maximize, nolog

sum logLL

// Second case: no missing values
ml model d0 weird_d0 (Model:  price = mpg foreign weight ) /lnsigma
ml maximize, nolog

sum logLL
di r(sum)

// Third case: add an empty observation, and exclude it.
replace logLL = .
count
set obs 75

ml model d0 weird_d0 (Model:  price = mpg foreign weight ) /lnsigma in 1/74
ml maximize, nolog

sum logLL
*************************************

. sysuse auto, clear
(1978 Automobile Data)

.
. gen logLL = .
(74 missing values generated)

.
. // First case: missing values in one variable (rep78)
. ml model d0 weird_d0 (Model:  price = rep78 foreign weight ) /lnsigma

. ml maximize, nolog
initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -756.67855
rescale:       log likelihood = -756.67855
rescale eq:    log likelihood = -754.07605

                                                  Number of obs   =         69
                                                  Wald chi2(3)    =      67.96
Log likelihood = -624.14723                       Prob > chi2     =     0.0000

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Model        |
       rep78 |   150.5707   312.1301     0.48   0.630    -461.1931    762.3345
     foreign |   3444.848   800.2654     4.30   0.000     1876.357     5013.34
      weight |   3.388606   .4113701     8.24   0.000     2.582335    4.194877
       _cons |  -5689.553   1724.021    -3.30   0.001    -9068.572   -2310.534
-------------+----------------------------------------------------------------
lnsigma      |
       _cons |   7.626673   .0851257    89.59   0.000      7.45983    7.793517
------------------------------------------------------------------------------

.
. sum logLL

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       logLL |         0

.
. // Second case: no missing values
. ml model d0 weird_d0 (Model:  price = mpg foreign weight ) /lnsigma

. ml maximize, nolog
initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -811.54531
rescale:       log likelihood = -811.54531
rescale eq:    log likelihood = -808.73926

                                                  Number of obs   =         74
                                                  Wald chi2(3)    =      73.87
Log likelihood = -670.09901                       Prob > chi2     =     0.0000

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Model        |
         mpg |    21.8536   72.18735     0.30   0.762     -119.631    163.3382
     foreign |    3673.06   665.2357     5.52   0.000     2369.222    4976.898
      weight |   3.464706   .6134653     5.65   0.000     2.262336    4.667076
       _cons |  -5853.696   3284.452    -1.78   0.075     -12291.1    583.7126
-------------+----------------------------------------------------------------
lnsigma      |
       _cons |   7.636454   .0821995    92.90   0.000     7.475346    7.797562
------------------------------------------------------------------------------

.
. sum logLL

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       logLL |        74   -9.055392    .9102058  -13.53397  -8.555032

. di r(sum)
-670.09904

.
.
. // Third case: add an empty observation, and exclude it.
. replace logLL = .
(74 real changes made, 74 to missing)

. count
   74

. set obs 75
obs was 74, now 75

.
. ml model d0 weird_d0 (Model:  price = mpg foreign weight ) /lnsigma in 1/74

. ml maximize, nolog
initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -811.54531
rescale:       log likelihood = -811.54531
rescale eq:    log likelihood = -808.73926

                                                  Number of obs   =         74
                                                  Wald chi2(3)    =      73.87
Log likelihood = -670.09901                       Prob > chi2     =     0.0000

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Model        |
         mpg |    21.8536   72.18735     0.30   0.762     -119.631    163.3382
     foreign |    3673.06   665.2357     5.52   0.000     2369.222    4976.898
      weight |   3.464706   .6134653     5.65   0.000     2.262336    4.667076
       _cons |  -5853.696   3284.452    -1.78   0.075     -12291.1    583.7126
-------------+----------------------------------------------------------------
lnsigma      |
       _cons |   7.636454   .0821995    92.90   0.000     7.475346    7.797562
------------------------------------------------------------------------------

.
. sum logLL

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       logLL |         0

.


2008/10/10 Eva Poen <eva.poen@gmail.com>:
> This is what I'm trying to implement:
> In my d0 likelihood evaluator, I would like to save the likelihood
> contribution (`lnf') for each observation in a variable, for later
> use. However, the newly created variable doesn't seem to be touched by
> the d0 evaluator, but there is no error message either.
>
> Here is what's in my estimation program:
> /*----mm3w0w1.ado---*/
> ...
>    // save likelihood?
>    if "`llsave'" != "" {
>        confirm new variable `llsave'
>        tempvar likesave
>        qui gen double `likesave' = .
>        global LL_save "`likesave'"
>    }
> ...
> ml model d0 ...
> ml maximize ...
> ...
>    // save likelihood?
>    if "`llsave'" != "" {
>        qui gen double `llsave' = `likesave'
>    }
> ...
> /*-------------------------*/
>
> where llsave() is an option to the program, which takes the new
> variable name. The idea is to save a (temporary, in case the program
> fails) variable name in $LL_save, and then process it in the
> likelihood evaluator. In the evaluator, I have:
>
> /*----mm3w0w1_d0.ado---*/
> ...
> ...
>      gen double `lnj' = ...
>
>       // save likelihood?
>       if "$LL_save" != "" {
>           replace $LL_save = `lnj'
>       }
>
>     mlsum `lnf' = `lnj'
> ...
> /*-------------------------*/
>
> The program runs fine (without error) and produces the expected
> results, apart from the new variable. This variable will still be what
> I set it to in mm3w0w1.ado in the first place. Can someone tell me
> where the mistake is? I have a feeling that I'm missing something very
> obvious.
>
>
> Thanks,
> Eva
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