I have the following predictions after running a logistic regression model on a set of molecules we suppose that are predictive of tumors versus normals.
Predicted class
T N
T 29 5
Actual class
N 993 912
I have a list of scores that range from predictions <0 (negative numbers) to predictions >0 (positive numbers). Then I have another column in my data.frame that indicated the labels (1== tumours and 0==normals) as predicted from the model. I tried to calculate the ROC using the library(ROC) in the following way:
pred = prediction(prediction, labels)
roc = performance(pred, "tpr", "fpr")
plot(roc, lwd=2, colorize=TRUE)
Using:
roc_full_data <- roc(labels, prediction)
rounded_scores <- round(prediction, digits=1)
roc_rounded <- roc(labels, prediction)
Call:
roc.default(response = labels, predictor = prediction)
Data: prediction in 917 controls (category 0) < 1022 cases (category1).
Area under the curve: 1
The AUC is equal to 1. I'm not sure that I run all correctly or probably I'm doing something wrong in the interpretation of my results because it is quite rare that the AUC is equal to 1.
There is a typo in your x.measure which should have thrown an error. You have "for" and not "fpr". Try the following code.
performance(pred, measure = "tpr", x.measure = "fpr")
plot(perf)
# add a reference line to the graph
abline(a = 0, b = 1, lwd = 2, lty = 2)
# calculate AUC
perf.auc <- performance(pred, measure = "auc")
str(perf.auc)
as.numeric([email protected])