Re: st: xtmixed with nonrtolerance. What happens?

["Lukas Bösch" <L.Boesch@gmx.de>]

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
> >
> > --
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> >
> 
> 
> 
> -- 
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
> 
> *
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