Re: st: Imputing for missing proportions

[Nick Cox <njcoxstata@gmail.com>]

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.
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