Clive--
I think Nick is referring to your claim that "you log-transform (LT)
the variable, transforming the scale from 0-1 to minus infinity-plus
infinity" which is incorrect--you mean to logit-transform the
variable, since the log transform ln(y) would turn a range of (0,1)
for y into (-inf, 0) for ln(y) and a logit transform would turn a
range of (0,1) for y into (-inf, +inf) for ln[y/(1-y)]. The -glm-
link option handles the transformation, so yes, -link(logit)- only
makes sense for models whose dependent variables are in [0,1] and
-link(log)- for y in [0,+inf)
--see also
http://statacorp.com/statalist/archive/2006-11/msg00294.html
http://statacorp.com/statalist/archive/2007-08/msg00177.html
and other messages in the "st: Binomial regression" thread for more options.
On 8/29/07, Clive Nicholas <[email protected]> wrote:
> Nick Cox wrote:
>
> > Some confusion here between logarithms and logits?
>
> If I'm thinking straight, you're arguing that the call to -glm,
> link(logit)- only makes sense for models whose dependent variables are
> already scaled 0-1, since the -link()- option does the transformation.
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