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Re: st: xtmixed with nonrtolerance. What happens?
From
Joerg Luedicke <[email protected]>
To
[email protected]
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" <[email protected]> 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 <[email protected]>
>> An: [email protected]
>> 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" <[email protected]> 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 <[email protected]>
>> >> An: [email protected]
>> >> 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" <[email protected]>
>> 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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>> >> > *
>> >> > * 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/
>> >> >
>> >>
>> >>
>> >>
>> >> --
>> >> 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/
>> >
>> > --
>> > 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
>> > * http://www.stata.com/support/statalist/faq
>> > * http://www.ats.ucla.edu/stat/stata/
>> >
>>
>> *
>> * For searches and help try:
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>
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