Re: st: Imputing for missing proportions

[Geomina Turlea <geomina@yahoo.fr>]

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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