labels are in classes:
> pred <- prediction( scores, classes )
> perf <- performance(pred, 'rec','prec')
> plot(perf)
>
> HTH,
> Tobias
>
> On 1/24/07, nitin jindal <[EMAIL PROTECTED]> wrote:
> > Hi,
> >
> > I am using logi
On 1/24/07, Frank E Harrell Jr <[EMAIL PROTECTED]> wrote:
>
> nitin jindal wrote:
>
> Using a cutoff is not a good idea unless the utility (loss) function is
> discontinuous and is the same for every subject (in the medical field
> utilities are almost never constant).
On 1/24/07, Frank E Harrell Jr <[EMAIL PROTECTED]> wrote:
> Why 0.5?
The probability has to adjusted based on some hit and trials. I just
mentioned it as an example
>
> Those are improper scoring rules that can be tricked. If the outcome is
> rare (say 0.02 incidence) you could just predict th
Hi,
I am using logistic regression model named lrm(Design)
Rite now I was using Area Under Curve (AUC) for testing my model. But, now I
have to calculate precision/recall of the model on test cases.
For lrm, precision and recal would be simply defined with the help of 2
terms below:
True Positive
Hi,
I am running a logistic regression model and then cross-validating it. But,
10-fold cross validation returns with following error message.
"Divergence or singularity in 5 samples"
Actually, I tried reading definition of divergence and singularity, but
could not understand it in context of my
If validate.lrm does not has this option, do any other function has it.
I will certainly look into your advice on cross validation. Thnx.
nitin
On 1/21/07, Frank E Harrell Jr <[EMAIL PROTECTED]> wrote:
>
> nitin jindal wrote:
> > Hi,
> >
> > I am trying to cross-
Hi,
I am trying to cross-validate a logistic regression model.
I am using logistic regression model (lrm) of package Design.
f <- lrm( cy ~ x1 + x2, x=TRUE, y=TRUE)
val <- validate.lrm(f, method="cross", B=5)
My class cy has values 0 and 1.
"val" variable will give me indicators like slope and
Hi,
I have to build a logistic regression model on a data set that I have. I
have three input variables (x1, x2, x3) and one output variable (y).
The syntax of lrm function looks like this
lrm(formula, data, subset, na.action=na.delete, method="lrm.fit",
model=FALSE, x=FALSE, y=FAL
