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From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Convergence never achieved with MI impute chained |

Date |
Thu, 21 Jun 2012 14:22:56 +0200 |

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 <lenalindbjergsperling@gmail.com> 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: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ -- -------------------------- 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/

**Follow-Ups**:**Re: st: Convergence never achieved with MI impute chained***From:*Lena Lindbjerg Sperling <lenalindbjergsperling@gmail.com>

**References**:**Fwd: st: Convergence never achieved with MI impute chained***From:*Lena Lindbjerg Sperling <lenalindbjergsperling@gmail.com>

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