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 a
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 posse
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 wh
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) abno
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 (o
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 varia
