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Re: st: to Marteen buis (independent variable)


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: to Marteen buis (independent variable)
Date   Sun, 9 Mar 2008 09:41:34 +0000 (GMT)

If you transform the independent variable you are changing the shape of
the relation between your depedent and independent variable. You can
see some examples of such shapes by using the -twoway function- graphs.
For instance, you asked about the log transformed x. You can see such a
shape by typing:

twoway function y = .1*ln(x), range(0 10)

You will see that a unit increase in x has a different impact depending
on where you start. A unit increase in x when you start from x=.5 has a
much bigger impact on y than when you start from x=8.

Fox (1991) has a nice introduction on this issue in term of linear
regression.

-- Maarten

John Fox (1991) Regression Diagnostics: An Introduction. Thoasand Oaks:
Sage. 


--- Mohammed El Faramawi <melfaram@yahoo.com> wrote:

> Hi Marteen,
> You wrote Anyhow the real issue with transforming the
> independent variables is whether the effect is linear,
> or gradually slowing down (log) u-shaped, quadratic,
> etc.' would you mind explaining more? especially what
> do you mean by slowing down and (log)
> Thank you .
> --- Maarten buis <maartenbuis@yahoo.co.uk> wrote:
> 
> > The independent variables don't need to be normally
> > distributed. If you
> > log transfom the variable than you think that if you
> > start with a small
> > dose, an increase has a much bigger effect than when
> > you start with a
> > larger, which may or may not make sense. Anyhow the
> > real issue with
> > transforming the indepedent variables is whether the
> > effect is linear,
> > or gradually slowing down (log) u-shaped, quadratic,
> > etc. As an aside,
> > if you compute percentile rank scores, you won't
> > make that variable
> > normal, uniformly distributed.
> > 
> > -- Maarten
> > 
> > --- Mohammed El Faramawi <melfaram@yahoo.com> wrote:
> > > I have  questions about the independent variable
> > > transformation. 
> > > I have an independent variable (lead mg/dl) which
> > is
> > > not normally distributed. I am trying to run it in
> > a
> > > cox hazard model the out come is mortality. What
> > > should I do with lead
> > > 1) Should I log transform it? I did that but it is
> > > still not normally ditributed after transformatin.
> > > 2) Leave it as it is because transformation does
> > not
> > > change the distribution?
> > > 3) categorize using quartiles or percentiles? 
> > 
> > 
> > -----------------------------------------
> > Maarten L. Buis
> > Department of Social Research Methodology
> > Vrije Universiteit Amsterdam
> > Boelelaan 1081
> > 1081 HV Amsterdam
> > The Netherlands
> > 
> > visiting address:
> > Buitenveldertselaan 3 (Metropolitan), room Z434
> > 
> > +31 20 5986715
> > 
> > http://home.fsw.vu.nl/m.buis/
> > -----------------------------------------
> > 
> > 
> >      
> >
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> 
> 
>      
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-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands

visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434

+31 20 5986715

http://home.fsw.vu.nl/m.buis/
-----------------------------------------


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