Schwarz,Paul wrote:
S-News readers,
I know that this is more of a statistical issue than an S-PLUS issue,
but I was hoping that someone would kindly summarize for me the issues
related to making predictions e.g, using predict(), involving models
with a log-transformed reponse variable. For example, if a linear model
is fitted using lm(log(y) ~ x1 + x2, data= ...), what is the proper way
to make predictions using the model? I've heard about a so-called
"smear" factor, but I'm not clear about what it is, or when to apply it,
or how to calculate it. For example, are there standard S-PLUS functions
for calculating a smear factor, or is there an option with the predict
functions? If someone would clarify this issue for me, I would be most
grateful.
Thank you for your time and consideration.
-Paul Schwarz
In the Hmisc library the areg.boot function has several smearing
features. But instead of log the Y transformation is more general
(nonparametric) as are the X-transformations. See also my book
Regression Modeling Strategies.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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