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Re: st: xtmixed with nonrtolerance. What happens?


From   Joerg Luedicke <joerg.luedicke@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: xtmixed with nonrtolerance. What happens?
Date   Thu, 23 Jun 2011 14:41:44 -0400

k stands for 1000 (as in kb=1000 bytes, for instance). What are your
Level 1 observations (i.e., the  6192)? If only 72 bears were exported
from the US in a given year then figures in the ballpark of hundreds
of thousands appear fairly high to me?

J.

On Thu, Jun 23, 2011 at 2:14 PM, "Lukas Bösch" <L.Boesch@gmx.de> wrote:
> In my opinion the scales dont differ wildly.
> I am not a statistician though, so maybe you have a different opinion.
>
>
> . sum centgdp2
>
>    Variable |       Obs        Mean    Std. Dev.       Min        Max
> -------------+--------------------------------------------------------
>    centgdp2 |      6192   -.0835699    .8318088  -.3333735   5.257175
>
> . sum centlandarea2
>
>    Variable |       Obs        Mean    Std. Dev.       Min        Max
> -------------+--------------------------------------------------------
> centlandar~2 |      6192   -.0336882    .9528875  -.6987395   2.490177
>
> . sum centpopulation2
>
>    Variable |       Obs        Mean    Std. Dev.       Min        Max
> -------------+--------------------------------------------------------
> centpopul~n2 |      6192   -.0018452    1.069818  -.6711841   8.741787
>
> . sum centyear2
>
>    Variable |       Obs        Mean    Std. Dev.       Min        Max
> -------------+--------------------------------------------------------
>   centyear2 |      6192           0    1.000024  -1.626886   1.626886
>
> . sum centforestarea2
>
>    Variable |       Obs        Mean    Std. Dev.       Min        Max
> -------------+--------------------------------------------------------
> centfores~a2 |      6192   -.0043667     1.00682 -2.396995   2.746216
>
> The dependent variable is export. The export of wild animal and plant products from one country to the rest of the world. For example: US export of Bears in 1992: 72.
> Because I cannot sum up the export of different species to one export figure, obviously bears and pearls are not the same, i have to deal with those mixed models. Socioeconomic factors are set as fixed effects and the genus and countries as the variable effects.
> As one species can be exported by different countries, the data is not hierarchic and country and genus are cross-classified. Or i think this is what it means. Two random effects at the same level for all observations. Joerge, can you explain what you mean with dividing by 100k? What does the k stand for?
>
> Thank you
>
> Lukas
>
> mixed modells-------- Original-Nachricht --------
>> Datum: Thu, 23 Jun 2011 09:47:55 -0400
>> Von: Joerg Luedicke <joerg.luedicke@gmail.com>
>> An: statalist@hsphsun2.harvard.edu
>> Betreff: Re: st: xtmixed with nonrtolerance. What happens?
>
>> Your model did not converge using the default convergence criteria and
>> with -nonrtolerance- you just turned off that default criteria
>> (though, I do not know what criteria is used instead?). However, you
>> should be very cautious with regard to the results.
>>
>> What is your dependent variable? From your output I gather that its
>> predicted mean is roughly 900k at average values of your covariates.
>> Maybe you should transform your dependent variable and fit the model
>> again (e.g., dividing it by 100k).
>>
>> A question in regards to your random effects: are -country- and
>> -genus- cross-classified?
>>
>> J.
>>
>> On Thu, Jun 23, 2011 at 6:21 AM, "Lukas Bösch" <L.Boesch@gmx.de> wrote:
>> > I transformed the data to z-scores (score-mean/stdeviation) before doing
>> the regression.
>> > What do you mean with differing scales? I have either percents, for
>> example % forest area, or absolute figures, for example land area, in my
>> dataset, but they are all transformed and should therefore be uniform.
>> > What about nonrtolerance?
>> >
>> > Thank you
>> >
>> > Lukas
>> >
>> > -------- Original-Nachricht --------
>> >> Datum: Wed, 22 Jun 2011 18:48:22 -0400
>> >> Von: Stas Kolenikov <skolenik@gmail.com>
>> >> An: statalist@hsphsun2.harvard.edu
>> >> Betreff: Re: st: xtmixed with nonrtolerance. What happens?
>> >
>> >> It looks like you have data with wildly differing scales. I understand
>> >> that you need to interpret the results in the original scales, but
>> >> maybe you could rescale your variables so that all of your
>> >> coefficients would be about 1. Whether that will help convergence is
>> >> anybody's telling, of course, but usually differences in the scales
>> >> (and hence coefficients) of the order of 1e3-1e4 are detrimental to
>> >> numeric convergence.
>> >>
>> >> On Wed, Jun 22, 2011 at 4:33 PM, "Lukas Bösch" <L.Boesch@gmx.de>
>> wrote:
>> >> > Dear Statalist community.
>> >> >
>> >> > I am using Stata 10.0 and doing a mixed model analysis of export
>> data.
>> >> > After trying different options and always having trouble to get a
>> >> propper output i finally found a way to get to my results. I however
>> could not
>> >> find any information about why it works and if it is allright. But let
>> us
>> >> first start with the problem:
>> >> >
>> >> > 1) This is the command i enter and the output stata creates:
>> >> >
>> >> > xtmixed quantity year centforestarea2 centgdp2 centlandarea2
>> >> centpopulation2 || _all: R.country || _all: R.genus
>> >> >
>> >> > Performing EM optimization:
>> >> >
>> >> > Performing gradient-based optimization:
>> >> >
>> >> > Iteration 0:   log restricted-likelihood = -77051.164
>> >> > Iteration 1:   log restricted-likelihood = -77046.704
>> >> > Iteration 2:   log restricted-likelihood = -77046.565
>> >> > Iteration 3:   log restricted-likelihood =   -77046.5
>> >> > Iteration 4:   log restricted-likelihood = -77046.468  (backed up)
>> >> > Iteration 5:   log restricted-likelihood =  -77046.46  (backed up)
>> >> > Iteration 6:   log restricted-likelihood = -77046.456  (backed up)
>> >> > Iteration 7:   log restricted-likelihood = -77046.454  (backed up)
>> >> > numerical derivatives are approximate
>> >> > nearby values are missing
>> >> > Iteration 8:   log restricted-likelihood = -77046.453  (backed up)
>> >> > numerical derivatives are approximate
>> >> > nearby values are missing
>> >> > Hessian has become unstable or asymmetric
>> >> >
>> >> > Mixed-effects REML regression                   Number of
>> obs
>> >>      =      6192
>> >> > Group variable: _all                            Number
>> of
>> >> groups   =         1
>> >> >
>> >> >
>> >>  Obs per group: min =      6192
>> >> >
>> >>               avg =    6192.0
>> >> >
>> >>               max =      6192
>> >> >
>> >>  Wald chi2(5)       =      9.26
>> >> > Log restricted-likelihood = -77051.164          Prob > chi2
>> >>    =    0.0991
>> >> >    quantity |      Coef.   Std. Err.      z    P>|z|
>> >> [95% Conf. Interval]
>> >> >        year |  -429.7599   215.8898    -1.99   0.047
>> >>  -852.8961   -6.623654
>> >> > centfores~a2 |  -9875.264   6631.861    -1.49   0.136
>> >>  -22873.47    3122.945
>> >> >    centgdp2 |  -2024.629   4138.469    -0.49   0.625
>> >>  -10135.88    6086.621
>> >> > centlandar~2 |  -52889.76   63817.96    -0.83   0.407
>> >>  -177970.7    72191.13
>> >> > centpopul~n2 |   22296.98   10234.72     2.18   0.029
>> >> 2237.304    42356.66
>> >> >       _cons |   895402.2   433369.4     2.07   0.039
>> >> 46013.74     1744791
>> >> >
>> >> >  Random-effects Parameters  |   Estimate   Std. Err.     [95%
>> >> Conf. Interval]
>> >> >
>> >> > _all: Identity               |
>> >> >               sd(R.country) |   313329.2          .
>> >> > _all: Identity               |
>> >> >                 sd(R.genus) |   6757.304          .
>> >> >                sd(Residual) |   60169.26          .
>> >> > LR test vs. linear regression:       chi2(2) =  7810.42   Prob >
>> >> chi2 = 0.0000
>> >> >
>> >> > Note: LR test is conservative and provided only for reference.
>> >> > Warning: convergence not achieved; estimates are based on iterated EM
>> >> >
>> >> > Obviously Stata has a problem and can't calculate the standard errors
>> of
>> >> the random factors.
>> >> >
>> >> > 2) With the option nonrtolerance it works however:
>> >> >
>> >> > xtmixed quantity year centforestarea2 centgdp2 centlandarea2
>> >> centpopulation2 || _all: R.country || _all: R.genus, nonrtolerance
>> >> >
>> >> > Performing EM optimization:
>> >> >
>> >> > Performing gradient-based optimization:
>> >> >
>> >> > Iteration 0:   log restricted-likelihood = -77051.164
>> >> > Iteration 1:   log restricted-likelihood = -77046.704
>> >> > Iteration 2:   log restricted-likelihood = -77046.565
>> >> > Iteration 3:   log restricted-likelihood =   -77046.5
>> >> > Iteration 4:   log restricted-likelihood = -77046.468  (backed up)
>> >> > Iteration 5:   log restricted-likelihood =  -77046.46  (backed up)
>> >> > Iteration 6:   log restricted-likelihood = -77046.456  (backed up)
>> >> >
>> >> > Computing standard errors:
>> >> >
>> >> > Mixed-effects REML regression                   Number of
>> obs
>> >>      =      6192
>> >> > Group variable: _all                            Number
>> of
>> >> groups   =         1
>> >> >
>> >> >
>> >>  Obs per group: min =      6192
>> >> >
>> >>               avg =    6192.0
>> >> >
>> >>               max =      6192
>> >> >
>> >> >
>> >> >
>> >>  Wald chi2(5)       =      9.22
>> >> > Log restricted-likelihood = -77046.456          Prob > chi2
>> >>    =    0.1008
>> >> >    quantity |      Coef.   Std. Err.      z    P>|z|
>> >> [95% Conf. Interval]
>> >> >        year |  -429.7645   216.4073    -1.99   0.047
>> >> -853.915   -5.614053
>> >> > centfores~a2 |  -9885.307    6647.52    -1.49   0.137
>> >>  -22914.21    3143.592
>> >> >    centgdp2 |  -2021.312   4148.464    -0.49   0.626
>> >>  -10152.15    6109.527
>> >> > centlandar~2 |  -52859.75   63778.66    -0.83   0.407
>> >>  -177863.6    72144.12
>> >> > centpopul~n2 |   22276.96   10257.46     2.17   0.030
>> >> 2172.715     42381.2
>> >> >       _cons |   895338.1   434389.3     2.06   0.039
>> >> 43950.68     1746726
>> >> >
>> >> >  Random-effects Parameters  |   Estimate   Std. Err.     [95%
>> >> Conf. Interval]
>> >> > _all: Identity               |
>> >> >               sd(R.country) |   313133.2    36075.6
>> >>  249840.9    392459.4
>> >> > _all: Identity               |
>> >> >                 sd(R.genus) |   3440.288   1355.694
>> >>  1589.157    7447.712
>> >> >                sd(Residual) |   60315.87   545.9681
>> >>  59255.23     61395.5
>> >> > LR test vs. linear regression:       chi2(2) =  7819.83   Prob >
>> >> chi2 = 0.0000
>> >> > Note: LR test is conservative and provided only for reference.
>> >> >
>> >> > Can someone explain to me why it works with nonrtolerance and tell me
>> if
>> >> these outputs are as reliable as if they were created without
>> >> nonrtolerance. I searched in the stata help and on stata.com but could
>> not find more
>> >> information about this.
>> >> >
>> >> > Kind regards
>> >> >
>> >> > Lukas
>> >> >
>> >> > --
>> >> > NEU: FreePhone - kostenlos mobil telefonieren!
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>> >> > *   http://www.ats.ucla.edu/stat/stata/
>> >> >
>> >>
>> >>
>> >>
>> >> --
>> >> Stas Kolenikov, also found at http://stas.kolenikov.name
>> >> Small print: I use this email account for mailing lists only.
>> >>
>> >> *
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>> >
>>
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