avas and ace

Hi,
Does any one know how we can decide on the correct transformation in (avas and ace)
after having drawn the graphs  y,g(y)   x ,s(x)  and g(y) ,s(x) . Is it possible by
only looking at patterns the graphs follow for example when
y ,g(y) shows a logaritmic pattern can we say that log transform on y is suitable?

It seems to me that the point of avas() and ace() is to arrive at
a transform that meets the criteria of each procedure but is not
necessarily based on some simple functional form like the
logarithm.  The lack of restriction to any particular functional
form is what is particularly useful.

You might, however, compare a simple function to the results of
avas or ace.  The output of each function consists of the
transformed values ty (the dependent variable) and a matrix of
predictors tx.  You also get y and x.  You can access these as
follows:

ace1 <- ace(..[all your stuff]..)
ace1$ty
ace1$y
etc.

If you think that ty is the log of y, then plot ty as a function
of log(y) and see if you get a straight line.  Or test for
linearity however you like.

Jonathan Baron, Professor of Psychology, University of Pennsylvania
Home page:            http://www.sas.upenn.edu/~baron
R page:               http://finzi.psych.upenn.edu/

On Tue, 20 Jan 2004 17:34:24 +0200

Hi,
Does any one know how we can decide on the correct transformation in
(avas and ace) after having drawn the graphs  y,g(y)   x ,s(x)  and g(y)
,s(x) . Is it possible by only looking at patterns the graphs follow for
example when y ,g(y) shows a logaritmic pattern can we say that log
transform on y is suitable? Thanks for your help.
Regards

The strategy I use is to not try to do this.  One reason is that you may
be tempted to fit a parametric model with such simple transformations,
without accounting for the hidden degrees of freedom from the uncertainty
in estimating the transformations.  The areg.boot function in the Hmisc
package will give you bootstrap confidence bands for ace and avas
transformations, taking into account almost all sources of uncertainty.

---
Frank E Harrell Jr   Professor and Chair           School of Medicine
                     Department of Biostatistics   Vanderbilt University