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From |
Nick Cox <njcoxstata@gmail.com> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: Imputing for missing proportions |

Date |
Fri, 12 Apr 2013 15:51:36 +0100 |

Good point. My comment was an easy shot, perhaps a cheap one. But I think I never imply that ignoring missing data is really an ideal solution, as I know it ignores the problem. Nick njcoxstata@gmail.com On 12 April 2013 15:44, Alan Acock <acock@me.com> wrote: > Nick is right that missing at random is a tough assumption, but it is easier than missing completely at random that is needed by listwise/case wise deletion. > Alan Acock > > Sent from my iPad > > On Apr 12, 2013, at 3:49 AM, Nick Cox <njcoxstata@gmail.com> wrote: > >> Well, imputation of missing values is vastly oversold any way. Missing >> at random? I don't (usually) believe it. (Highly unofficial opinion.) >> Nick >> njcoxstata@gmail.com >> >> >> On 12 April 2013 11:44, Geomina Turlea <geomina@yahoo.fr> wrote: >>> I know, but - mi impute- does not support glm either >>> >>> _________________________________________Geomina Turlea >>> TODO AQUEL QUE SUEÑA SE CONVIERTE EN ARTISTA >>> >>> >>> --- On Fri, 4/12/13, Nick Cox <njcoxstata@gmail.com> wrote: >>> >>>> From: Nick Cox <njcoxstata@gmail.com> >>>> Subject: Re: st: Imputing for missing proportions >>>> To: "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> >>>> Date: Friday, April 12, 2013, 1:35 PM >>>> I haven't looked at whether it mixes >>>> with -mi-, but -glm- with >>>> -link(logit)- is a standard way to handle continuous >>>> proportions. >>>> >>>> Nick >>>> njcoxstata@gmail.com >>>> >>>> >>>> On 12 April 2013 11:08, Geomina Turlea <geomina@yahoo.fr> >>>> wrote: >>>>> Maarten, >>>>> Thank you very much for your answer. >>>>> The problem with -mi impute - is that it does not >>>> really have an option for regressing proportions. I can't >>>> really use truncated regression, and my dependent variable >>>> is not binary or categorial, but a continous variable betwen >>>> 0 and 1. >>>>> I am considering to simulate the multiple imputation >>>> with a beta regression for estimation of the missing >>>> values. >>>>> Very gratefull for an yes/no opinion on this, >>>>> Geomina >>>>> >>>>> >>>>> --- On Thu, 4/11/13, Maarten Buis <maartenlbuis@gmail.com> >>>> wrote: >>>>> >>>>>> From: Maarten Buis <maartenlbuis@gmail.com> >>>> >>>> Geomina Turlea wrote: >>>> >>>>>>> I am fighting for a while with estimate >>>> missing data >>>>>> for the share of ICT professionals/total >>>> employment, in 59 >>>>>> industries, 27 EU countries and for 14 years. >>>>>>> This data exists in the European Labour Force >>>> Survey, >>>>>> but the dataset is incomplete. >>>>>>> >>>>>>> 1. Can I use mi impute with proportions? >>>>>>> 2. I used betafit to fit a distribution with >>>> values >>>>>> between 0 and 1. Than I imputed the missing values >>>> from the >>>>>> estimated beta distribution. Is this method >>>>>> superior/inferior to using mi impute? >>>>>>> 3. I tried to use the Kolmogorov-Smirnov test, >>>> but I >>>>>> don't know what I got wrong. Below is a sequence >>>> where I >>>>>> created a variable with the distribution beta and >>>> then test >>>>>> the hypothesis with the K-S test. The test rejects >>>> the null >>>>>> hypothesis that the data has the distribution I >>>> used to >>>>>> create it. How could that be? >>>>>>> >>>>>>> . gen x=rbeta(0.05, 1.77) >>>>>>> . ksmirnov x=rbeta(0.05, 1.77) >>>> >>>>>> My first step would be to look at the industries >>>> with >>>>>> missing values. >>>>>> Sometimes missing just means 0 or negligable, and >>>> looking at >>>>>> the >>>>>> industries would give you a fair guess of whether >>>> that is >>>>>> the case. If >>>>>> that is the case your imputation problem reduces to >>>> just a >>>>>> recoding >>>>>> problem. >>>>>> >>>>>> For questions 2 and 3: If you have an imputation >>>> problem, >>>>>> then you >>>>>> should use -mi- and not -betafit- (available from >>>> SSC), >>>>>> because that >>>>>> is what -mi- was designed for. >>>>>> >>>>>> For question 3: -rbeta()- gives you random numbers >>>> from a >>>>>> beta >>>>>> distribution, so that is definately not something >>>> you want >>>>>> to feed in >>>>>> -ksmirnov-. I just would use either -margdistfit- >>>> or >>>>>> -hangroot- (also >>>>>> available from SSC) after -betafit- to check the >>>> fit. >>>> * >>>> * For searches and help try: >>>> * http://www.stata.com/help.cgi?search >>>> * http://www.stata.com/support/faqs/resources/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/faqs/resources/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/faqs/resources/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/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: Imputing for missing proportions***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Imputing for missing proportions***From:*Geomina Turlea <geomina@yahoo.fr>

**Re: st: Imputing for missing proportions***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Imputing for missing proportions***From:*Alan Acock <acock@me.com>

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