Michael Dewey wrote:
At 17:12 09/04/06, Ramón Casero Cañas wrote:
I am not sure what the problem you really want to solve is but it seems
that
a) abnormality is rare
b) the logistic regression predicts it to be rare.
If you want a prediction system why not try different cut-offs (other
On Sun, 2006-04-16 at 19:10 +0100, Ramón Casero Cañas wrote:
Thanks for your suggestions, Michael. It took me some time to figure out
how to do this in R (as trivial as it may be for others). Some comments
about what I've done follow, in case anyone is interested.
The problem is a)
Ramón Casero Cañas wrote:
Michael Dewey wrote:
At 17:12 09/04/06, Ramón Casero Cañas wrote:
I am not sure what the problem you really want to solve is but it seems
that
a) abnormality is rare
b) the logistic regression predicts it to be rare.
If you want a prediction system why not try
Frank E Harrell Jr wrote:
This makes me think you are trying to go against maximum likelihood to
optimize an improper criterion. Forcing a single cutpoint to be chosen
seems to be at the heart of your problem. There's nothing wrong with
using probabilities and letting the utility possessor
Ramón Casero Cañas wrote:
Frank E Harrell Jr wrote:
This makes me think you are trying to go against maximum likelihood to
optimize an improper criterion. Forcing a single cutpoint to be chosen
seems to be at the heart of your problem. There's nothing wrong with
using probabilities and
At 17:12 09/04/06, Ramón Casero Cañas wrote:
I have not seen a reply to this so far apologies if I missed something.
When fitting a logistic regression model using weights I get the
following warning
data.model.w - glm(ABN ~ TR, family=binomial(logit), weights=WEIGHT)
Warning message:
When fitting a logistic regression model using weights I get the
following warning
data.model.w - glm(ABN ~ TR, family=binomial(logit), weights=WEIGHT)
Warning message:
non-integer #successes in a binomial glm! in: eval(expr, envir, enclos)
Details follow
***
I have a binary dependent
