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Re: st:Transformation for skewed variables with negative values?
Thanks for your comment
----- Original Message -----
From: "Nick Cox" <email@example.com>
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-.
> 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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