On Oct 31, 2009, at 7:29 AM, tdm wrote:
OK, I think I've figured it out, the predict of lrm didn't seem to
pass it
through the logistic function. If I do this then the value is
similar to
that of lm. Is this by design?
Yes, at least for certain meanings of "this". When working with
probabilities or propotions as the dependent variable, the estimates
will be similar in the central regions of the data but diverge at the
extremes, although it is linear regression that blows up when
estimating probabilities. Logistic regression is by design constrained
to predict a true probability even outside the range of the data,
while the "prediction" for an ordinary least squares model has no such
constraint.
Why would it be so?
1 / (1 + Exp(-1 * 3.38)) = 0.967
I am guessing that you have not read the help page for predict.lrm. In
it Harrell clearly indicates that the default output from that
function is of type "lp" or the linear predictor. Since you were
apparently unaware that logistic regression and simple linear
regression were different approaches to modeling, then you are
probably also unaware that you need to use the inverse of the logistic
transformation on the linear predictor, which expressed on the log
odds scale, to get back a probability estimate.
tdm wrote:
Anyway, do you know why the lrm predict give me a values of 3.38?
==
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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