Custom AUC in R with different thresholds and binary predictions - r

Question

I am looking to plot a FPR vs TPR point on an AUC graph for different thresholds.
For example, if data$C2 is my data frame with the true response column (either 0 or 1), I want to make a vector with predicted values (0 or 1) when data$C1 (a different measurement column) is above or below the specified threshold. Here is the function I've attempted with the ROCR package.
fun <- function (data, col1, col2){
perfc <- NULL #Create null vectors for prediction and performance
perfs <- NULL
temp <- NULL
d <- seq(0.10,0.30,0.01) ##Various thresholds to be tested
for (i in length(d){
temp <- ifelse(data[,col1] > d, 1 , 0) ##Create predicted responses
pred <- prediction(temp, data[,col2]) #Predict responses over true values
perf <- performance(pred, "tpr","fpr") #Store performance information
predc[i] <- pred #Do this i times for every d in the sequence
perfc[i] <- perf
preds <- prediction.class(predc, col2) #Combine to make prediction class
perfs <- performance.class(preds, "tpr","fpr") #Combine to make performance class
}
plot(perfs) #Plot TPR against FPR
}
Is the problem because temp is a list vector and the true labels are from a matrix? Am I applying this for loop incorrectly?
Thanks in advance!
Edit: Here's my attempt to do this manually without the ROC package.
for(t in seq(0.40,0.60,0.01)) #I want to do this for every t in the sequence
{
t <- t
TP <- 0
FP <- 0
p <- sum(data$C2==1, na.rm=TRUE) #Total number of true positives
n <- sum(data$C2==0, na.rm=TRUE) #Total number of true negatives
list <- data$C1 #Column to vector
test <- ifelse(list > t, 1, 0) #Make prediction vector
for(i in 1:nrow(data))
{if(test==1 & data$C2==1)
{TP <- TP + 1} #Count number of correct predictions
if(test==1 & data$C2==0)
{FP <- FP + 1} #Count number of false positives
}
plot(x=(FP/n),y=(TP/p)) #Plot every FP,TP pair
}

Answer 1

I hope I understand your question right, but I think that by AUC graph you mean ROC curve. The ROC curve already takes into account different thresholds to make those classification decisions. See this wikipedia page. I found this picture particularly helpful.
If the above is right, then all you need to do in your code is:
pred <- prediction(data[,col1], data[,col2])
perf <- performance(pred, "tpr","fpr")
plot(perf)
If you would like to 'add' a different curve to that plot, perhaps because you used a different classification technique (e.g. decision tree instead of logistic regression. Then use plot(perf2,add=TRUE). Where perf2 is created in a same way as perf. See the documentation.