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From |
Alan Acock <acock@me.com> |

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

Subject |
Re: st: Imputing for missing proportions |

Date |
Fri, 12 Apr 2013 07:44:54 -0700 |

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/

**Follow-Ups**:**Re: st: Imputing for missing proportions***From:*Maarten Buis <maartenlbuis@gmail.com>

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

**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>

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