[R] ROCR package question for evaluating two regression models
f.harrell at vanderbilt.edu
Sat Sep 3 15:24:35 CEST 2011
It is not possible to have one cutoff point unless you have a very strange
utility function. Nor is there a need for a cutoff when using a probability
It is not advisable to compare models based on ROC area as this loses power.
A likelihood-based approach is recommended.
Andra Isan wrote:
> Hello All,
> I have used logistic regression glm in R and I am evaluating two models
> both learned with glm but with different predictors. model1 <- glm (Y ~
> x4+ x5+ x6+ x7, data = dat, family = binomial(link=logit))model2 <- glm
> (Y~ x1 + x2 +x3 , data = dat, family = binomial(link=logit))
> and I would like to compare these two models based on the prediction that
> I get from each model:
> pred1 = predict(model1, test.data, type = "response")pred2 =
> predict(model2, test.data, type = "response")
> I have used ROCR package to compare them:pr1 = prediction(pred1,test.y)pf1
> = performance(pr1, measure = "prec", x.measure = "rec") plot(pf1) which
> cutoff this plot is based on?
> pr2 = prediction(pred2,test.y)pf2 = performance(pr2, measure = "prec",
> x.measure = "rec")pf2_roc = performance(pr2,measure="err")plot(pf2)
> First of all, I would like to use cutoff = 0.5 and plot the ROC,
> precision-recall curves based on that cutoff value. In other words, how to
> define a cut off value in performance function?For example, in pf2_roc =
> performance(pr2,measure="err"), when I do plot(pf2_roc), it plots for
> every single cutoff point. I only want to have one cut off point, is there
> any way to do that?Second, I would like to see the performance of the two
> models based on the above measures on the same plot so the comparison
> would be easier. In other words, how can I plot (pf1, pf2) and compare
> them together?plot(pf1, pf2) would give me an error as follows:Error in
> as.double(x) : cannot coerce type 'S4' to vector of type 'double'
> Could you please help me with that?
> Thanks a lot,Andra
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Department of Biostatistics, Vanderbilt University
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