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# RE: st: Proportional Independent Variables

 From nick bungy To Subject RE: st: Proportional Independent Variables Date Thu, 28 Feb 2013 13:19:36 +0000

```Thank you for your responses,
My thoughts following this discussion are the following:
1. Apply a logratio transformation to the data in the short run
2. Look into a simplex mixture approach as a longer term aspiration, given my data does have a very large amount of 0's. I noticed the topic was mentioned in the book you kindly linked Nick, so that will be my first avenue to explore.
Best,
Nick

----------------------------------------
> Date: Thu, 28 Feb 2013 07:35:23 -0500
> Subject: Re: st: Proportional Independent Variables
> From: jvverkuilen@gmail.com
> To: statalist@hsphsun2.harvard.edu
>
> On Thu, Feb 28, 2013 at 4:19 AM, Nick Cox <njcoxstata@gmail.com> wrote:
> >
> > 2. For different reasons log and logit transformations might be
> > considered. There is a very inward-looking literature on compositional
> > data analysis centred on more exotic transformations tailored to the
> > problem. The reference I gave earlier is one entry into that.
>
> I was going to throw out the same reference. It's not a trivial
> problem, but a narrow one due to the way it's been written. But the
> walkaway message of most of it is that the log-ratio transformation is
> the most reasonable one. This all just works out to being logit if you
> only had two, or log-odds. The logic is very similar to the
> multinomial logit, with the same difficult dependence structure.
>
>
>
> > 3. The two previous points are often complicated by measured zeros.
> > There is then a long slow agony about whether they are structural or
> > sampling zeros and what to do about them. The more components are
> > measured, the worse this usually gets, whether it is a fractions of a
> > budget spent on different things, or proportions of a material by
> > elements or compounds or particle size classes, or whatever.
>
> Yes, this is a real issue, and unfortunately the transformations used
> can create huge outlier problems, just like log transforms do when
> there's a 0 value.
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```