This code is untested, since you did not provide any example data.  But it
may help you get started.

Jean

mydata <- read.csv(file.choose(), header=TRUE)
library(ROCR)  # ROC curve and assessment of my prediction

plot(0:1, 0:1, type="n", xlab="False positive rate", ylab="True positive
rate")
abline(0, 1, col="red")

nsim <- 5
auc <- rep(NA, nsim)
for(i in 1:nsim) {
select <- sample(nrow(mydata), round(nrow(mydata)*0.7))
 data70 <- mydata[select, ]  # train
data30 <- mydata[-select, ]  # test
temp.glm <- glm(Death ~ Temperature, data=data70, family=binomial)
 pred <- prediction(data30$pred, data30$Death)
perf <- performance(pred, "tpr", "fpr")
 plot(perf, add=TRUE)
auc[i] <- attributes(performance(pred, "auc"))$y.values[[1]] # area under
the ROC
 }
auc


On Tue, Jul 2, 2013 at 3:25 AM, Eddie Smith <eddieatr@gmail.com> wrote:

> Guys,
>
> I select 70% of my data and keep 30% of it for model validation.
>
> mydata <- read.csv(file.choose(), header=TRUE)
> select <- sample(nrow(mydata), nrow(mydata) * .7)
> data70 <- mydata[select,]  # select
> data30 <- mydata[-select,]  # testing
> temp.glm <- glm(Death~Temperature, data=data70,
> family=binomial(link="logit"))
>
> library(ROCR)  # ROC curve and assessment of my prediction
> pred <- prediction(data30$pred, data30$Death)
> perf <- performance(pred,"tpr","fpr")
> plot(perf); abline(0, 1, col="red")
> attributes(performance(pred, 'auc'))$y.values[[1]] # area under the ROC
>
> How do i make a loop so that the process could be repeated several time,
> producing randomly ROC curve and under ROC values?
>
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>
> ______________________________________________
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> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

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