Hi R users,
I am sort of new on those.
In the following logistic regression function "tigol( 0.3 + 3*x + 5*z + 7*w
) ", why the intercept was set 0.3, and the coefficients were set 3 ,5 and
7?
Thanks.
--
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http://r.789695.n4.nabble.com/ROCR-for-combination-of-marke
Hi Eik or other who might help:
I got this error:
"Error in roc.formula(form = y1 ~ x + z, plot = "ROC") :
Invalid formula: exactly 1 predictor is required in a formula of type
response~predictor.
"
when I ran "out=ROC( form = y1 ~ x + z, plot="ROC") " from your code.
How to fix it?
Thanks.
-
Thanks Eik. As you said both packages give the same result, except labelling of
the x axis.
--- On Thu, 28/4/11, Eik Vettorazzi wrote:
From: Eik Vettorazzi
Subject: Re: [R] ROCR for combination of markers
To: "Rasanga Ruwanthi"
Cc: "R Help"
Date: Thursday, 28 Apr
t for (1-sensitivity) vs specificity?
>
> Thanks
> Rasanga
>
>
>
> --- On *Thu, 28/4/11, Eik Vettorazzi
> //* wrote:
>
>
> From: Eik Vettorazzi
> Subject: Re: [R] ROCR for combination of markers
> To: "Rasanga Ruwanthi"
> Cc: &
ol( 0.3 + 3*x + 5*z + 7*w ) )
> out=ROC( form = y1 ~ x + z, plot="ROC",MI=FALSE)
>
> But this function does not produce SE or CI of the AUC or any other
> statistics. Any suggestion to get these?
>
> Thanks again
> Rasanga
>
>
> --- On *Thu, 28/4/11, E
tigol( 0.3 + 3*x + 5*z + 7*w ) )
out=ROC( form = y1 ~ x + z, plot="ROC",MI=FALSE)
But this function does not produce SE or CI of the AUC or any other
statistics. Any suggestion to get these?
Thanks again
Rasanga
--- On Thu, 28/4/11, Eik Vettorazzi wrote:
From: Eik Vettorazzi
Be careful not to use cutpoints at any stage. improveProb does not require
this. ROC analysis on the other hand is dangerous in the sense that it
tempts one to find cutpoints, which are not replicable or consistent with
decision theory.
Frank
Eik Vettorazzi wrote:
>
> Hi Rasanga,
> you may ha
... and additionally, 'ROC' from the Epi package does the second step
all in one.
Am 28.04.2011 13:01, schrieb Eik Vettorazzi:
> Hi Rasanga,
> you may have a look at the 'improveProb' function from the Hmisc
> package. There you can compare the increase in prognostic power for
> several combinatio
Hi Rasanga,
you may have a look at the 'improveProb' function from the Hmisc
package. There you can compare the increase in prognostic power for
several combinations of markers. You can create a ROC curve for a
combination of markers by using the predicted risks eg. from a logistic
regression model
Dear list
I have 5 markers that can be used to detect an infection in combination. Could
you please advise me how to use functions in ROCR/ other package to produce the
ROC curve for a combination of markers?
I have used the following to get ROC statistics for each marker.
pred <- prediction(
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