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Re: st: Convergence never achieved with MI impute chained
From
Lena Lindbjerg Sperling <[email protected]>
To
[email protected]
Subject
Re: st: Convergence never achieved with MI impute chained
Date
Fri, 22 Jun 2012 13:07:03 +0200
Thank you Maarten.
I am in developing countries:-) And what we are trying to explore is the development in wages and movement of workers across sectors, so I can't really destroy the industry category as this is our final interest. It can't predict that if there are no self-employed in the mining sector, then it should just not assign any of them to that sector? I mean how empty is too empty?
Hmm I have to think of other possibilities...
Best,
Lena
Den Jun 21, 2012 kl. 2:22 PM skrev Maarten Buis:
> I have just seen that you used only the first digit of the ISIC
> classification. However, it contains lots of sparse categories(*).
> These will cause problems. Also inspect education for sparse
> categories. You'll need to combine those sparse categories with
> "adjacent" categories in order to get sufficiently filled cells. Also
> look at a cross tabulation of industry and education, and see if there
> aren't any cells that are too empty. That will probably mean a second
> round of merging categories.
>
> I would not use ordered models or mvn for imputing industry, that just
> does not make sense.
>
> Hope this helps,
> Maarten
>
> (*) If this is recent data from a western country than you have made a
> coding error. In that case there are way way way too many farmers.
>
> On Thu, Jun 21, 2012 at 1:46 PM, Lena Lindbjerg Sperling
> <[email protected]> wrote:
>>>
>>> Thank you for your answer!
>>>
>>> It does seem though that all occupations are represented in both private and public sectors.
>>> And I also have another data set where I only impute educational level, industry (ISIC 3 category) and wage and I still get not convergence, even though that's just one mlogit, one ologit and one pmm...so that doesn't seem to be the problem.
>>>
>>> I got a result out for the mi xeq 0: mlogit for industry however and it looks like this:
>>> -> mlogit industry
>> Iteration 0:00 log likelihood = -4875.9554
>> Iteration 1:00 log likelihood = -4875.9554
>> Multinomial logistic regression Number of obs =
>> LR chi2(0) = 0
>> Prob > chi2 = .
>> Log likelihood = -4875.9554 Pseudo R2 =
>> industry Coef. Std. Err. z P>z [95%
>> Agriculture__Hunting__etc_ (base outcome)
>> Mining
>> _cons -4.982464 0.2896632 -17.2 0 -5.550194 -4.414735
>> Manufacturing
>> _cons -2.671581 0.0939994 -28.42 0 -2.855816 -2.487345
>> Public_services
>> _cons -3.42432 0.134593 -25.44 0 -3.688117 -3.160522
>> Construction
>> _cons -3.204691 0.1210617 -26.47 0 -3.441968 -2.967415
>> Retail__Hotels
>> _cons -1.714798 0.0612048 -28.02 0 -1.834758 -1.594839
>> Transport_and_telecomnunications
>> _cons -4.759321 0.2593031 -18.35 0 -5.267546 -4.251096
>> Finance_and_business_serv_
>> _cons -6.368759 0.5778449 -11.02 0 -7.501314 -5.236204
>> Communal_services
>> _cons -0.830113 0.0433825 -19.13 0 -0.9151412 -0.7450848
>> Others_not_well_specified
>> _cons -1.753638 0.0622235 -28.18 0 -1.875594 -1.631683
>>>
>>> Should I use something else to impute this? It runs from 1 to 10 so maybe ordered is better? I get convergence if I use ordered logit for industry and occupation. They really shouldn't be ordered, but how important is that choice?
>>>
>>>
>>> I can get results out if I use mvn, but is that a very bad idea? Seems like the literature disagrees quite a bit on how severe it is to assume normality?
>>>
>>> Best,
>>> Lena
>>>
>>> Den Jun 21, 2012 kl. 10:48 AM skrev Maarten Buis:
>>>
>>>> On Thu, Jun 21, 2012 at 10:15 AM, Lena Lindbjerg Sperling wrote:
>>>>> I just looked at the mail again, and the data is not as bad as it looks, as I'm only imputing on the employed population (lstatus==1) and when we only look at them mi describe shows:
>>>>> mi describe
>>>>>
>>>>> Style: wide
>>>>> last mi update 21jun2012 10:03:51, 18 seconds ago
>>>>>
>>>>> Obs.: complete 2,702
>>>>> incomplete 912 (M = 0 imputations)
>>>>> ---------------------
>>>>> total 3,614
>>>>>
>>>>> Vars.: imputed: 7; occup(126) ocusec(144) whours(167) edulevel(171) ocu(228) industry(204) mwage(598)
>>>>
>>>> Just looking at the variable names I suspect that this is an extremely
>>>> hard model to estimate. How many categories do the variables occup,
>>>> ocusec, ocu, and industry have? Are there combinations of three or
>>>> less of these that for some observations perfectly predict one or more
>>>> remaining variables? For example, if we know that someone is a mayor
>>>> than we also know that (s)he is working in the public sector.
>>>>
>>>> <snip>
>>>>> Iteration 14: log pseudolikelihood = -2454486.7 (not concave)
>>>>> Not completely sure what this means. Can you see where things are wrong from this?
>>>>
>>>> It means that this sub-model did not converge, probably because of the
>>>> problems indicated above.
>>>>
>>>>> When I use -mi xeq 0: mlogit - the result is:
>>>>> m=0 data:
>>>>> -> mlogit
>>>>> last estimates not found
>>>>> r(301);
>>>>>
>>>>> But I thought it was the observed data...which should be there?
>>>>
>>>> What you asked for was for Stata to replay the last -mlogit- command,
>>>> and it replied that the last command wasn't -mlogit-. You probably
>>>> pressed break before the model finished estimating, which makes sense
>>>> if it did not converge.
>>>>
>>>> Hope this helps,
>>>> Maarten
>>>>
>>>> --------------------------
>>>> Maarten L. Buis
>>>> Institut fuer Soziologie
>>>> Universitaet Tuebingen
>>>> Wilhelmstrasse 36
>>>> 72074 Tuebingen
>>>> Germany
>>>>
>>>>
>>>> http://www.maartenbuis.nl
>>>> --------------------------
>>>>
>>>> *
>>>> * 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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>
>
>
> --
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
>
> http://www.maartenbuis.nl
> --------------------------
>
> *
> * 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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