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Re: st:Transformation for skewed variables with negative values?


From   "woong-tae chung" <Woong.Chung@colorado.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   Re: st:Transformation for skewed variables with negative values?
Date   Sun, 15 Aug 2004 23:46:22 -0600

Thanks for your comment
WT 
----- Original Message ----- 
From: "Nick Cox" <n.j.cox@durham.ac.uk>
To: <statalist@hsphsun2.harvard.edu>
Sent: Tuesday, August 15, 2006 3:16 PM
Subject: RE: st:Transformation for skewed variables with negative values?


> I agree with Joseph's general stance on this question. 
> Also, consider the alternative of a non-identity link 
> and a -glm-, which often offers the advantages of
> a transformation without the disadvantages. 
> 
> However, more generally, I will add a plug for 
> my package -transint- from SSC, which is just 
> a help file with various comments on transformations. 
> You can install it using -ssc-. 
> 
> Nick 
> n.j.cox@durham.ac.uk 
> 
> Joseph Coveney
>  
> > Woong Chung wrote:
> > 
> > I need following help. I have panel dataset for estimating a 
> > simple linear
> > equation.
> > The problem is that my all variables have sknewness and big 
> > variation(large
> > std).
> > In particualr, the dependent variable and one of independant 
> > variables have
> > a negative sknewness, while all other independant variables 
> > are shown by
> > positive sknewness. My first intension is using a log 
> > transformation of all
> > variables  but seems not to be a good idea since all 
> > variables have negative
> > values (around 20%)
> > Besides, all variables except one of independant variables 
> > are ratio, thus
> > that idea would make worse.
> > 
> > I would be so glad if anyone has suggestions to solve this problem
> > 
> > --------------------------------------------------------------
> > ------------------
> > 
> > It's not clear that you actually have a problem.
> > 
> > It shouldn't be a problem that your independent variables are 
> > skewed or have 
> > a wide distribution.  There isn't any assumption their 
> > distribution, and it 
> > is considered better to for them to cover more ground.  
> > They're only assumed 
> > not to comprise a linear combination within machine 
> > precision.  (There are 
> > other assumptions about them, in particular, about their 
> > relation to the 
> > random effects, but that's another matter.)
> > 
> > Fit the model as-is.  Examine the residuals and empirical 
> > Bayes predictions.
> > If these do not have a reasonably normal-appearing distribution, then
> > transform the dependent variable in accordance with shaping-up their
> > distributions, and not the dependent variable's distribution per se.
> > 
> > Also, from your description, it seems that your dependent 
> > variable is a 
> > ratio.  Consider sticking its denominator in the model as a 
> > predictor and 
> > using its numerator as the dependent variable.
> 
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