Thank you very much Maarten
--- Maarten buis <[email protected]> wrote:
> 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 <[email protected]> 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 <[email protected]> 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 <[email protected]>
> 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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>
> -----------------------------------------
> 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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