Re: st: xtmixed with nonrtolerance. What happens?

["Lukas Bösch" <L.Boesch@gmx.de>]

sum quantity

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    quantity |      6192    8545.829    116704.3          0    4019264

and here transformed:
 sum centquantity2

    Variable |       Obs        Mean    Std. Dev.       Min        Max
        
-------------+--------------------------------------------------------
 centquanti~2 |      6192    2.17e-09           1  -.0732263   34.3665
   

The dependent variable is however not a real count. It was a count in the original dataset, or i think so, but i had to transform the original data and i am not sure wheter it still is a count as it involves dezimals, and transformed it even has negative numbers. I tried the xtmepoisson but it did not work at all. I didn't had the time to work on it as i had to go to work, so i will try it later. But for 1 thing, I surely cant work with the z-scores in xtmepoisson as negative numbers aren't alloud. The untransformed dependent variable also seems not to fit to the xtmepoisson criterias and it doesnt even start to calculate.

The sample population are 40 countries. I started with 130 but because of the complexity of the data and because of all the missings i am working with 40 countries now. I have been told that i need a completely balanced data, and as i still have a representativ sample of countries i thought it would be allright. Having no missings in the data cant be wrong, was my thought.

Thanks

Lukas


-------- Original-Nachricht --------
> Datum: Fri, 24 Jun 2011 08:54:45 -0400
> Von: Anders Alexandersson <andersalex@gmail.com>
> An: statalist@hsphsun2.harvard.edu
> Betreff: Re: st: xtmixed with nonrtolerance. What happens?

> I too would like to see the frequency distribution of your original
> dependent variable (i.e., before standardization).
> and, like Maarten, I would use xtmepoisson instead of xtmixed if the
> outcome variable is a count.
> 
> Lukas, what is your sample population, e.g, the 40 or the 130 countries?
> Why do you need completely balanced data (no missing values)?
> 
> Anders Alexandersson
> andersalex@gmail.com
> 
> Lukas wrote:
> > 1) Concerning the missings, i took care to delete all data i don't have
> the complete time series for.
> > This means that i had to drop from 130 countries over a 20 years period
> to 40 countries over a 15 years
> > period. On the other hand, there are deffinitely no missing values.
> 
> On Fri, Jun 24, 2011 at 6:30 AM, Maarten Buis <maartenlbuis@gmail.com>
> wrote:
> > --- On Fri, Jun 24, 2011 at 12:11 PM, "Lukas Bösch" wrote:
> >> One more information:
> >>
> >> When i run the modell only with the complete export time series.
> Without any zeroes in the dependent data, the output looks like this:
> > <snip>
> >>                 sd(R.genus) |   2.93e-06   .0221629      
>       0
> >
> > I would not trust that result. First, leaving out 0s means you select
> > on your dependent variable, which is a very bad idea. Second, your
> > results indicate that the genus variation is de facto 0 (2.93e-6 =
> > 0.000000293), which given your context is very unlikely.
> 
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