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Re: st: xtmixed with nonrtolerance. What happens?


From   "Lukas Bösch" <L.Boesch@gmx.de>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: xtmixed with nonrtolerance. What happens?
Date   Thu, 23 Jun 2011 20:14:30 +0200

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!
> >> > Jetzt informieren: http://www.gmx.net/de/go/freephone
> >> > *
> >> > *   For searches and help try:
> >> > *   http://www.stata.com/help.cgi?search
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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.
> >>
> >> *
> >> *   For searches and help try:
> >> *   http://www.stata.com/help.cgi?search
> >> *   http://www.stata.com/support/statalist/faq
> >> *   http://www.ats.ucla.edu/stat/stata/
> >
> > --
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> > Jetzt informieren: http://www.gmx.net/de/go/freephone
> > *
> > *   For searches and help try:
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> >
> 
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