plot error for aucpr with ROCR package

[middeleast20]

Dear all,
could anyone help to find the solution for this error, and does AUC should equal the misclassification error or no?

rm(list = objects())
iris$Species <- factor(iris$Species,

•                        levels = c("setosa", "versicolor", "virginica"), 
  

•                        labels = c(0, 1, 1))
  

ix = sample(nrow(iris), nrow(iris)/3)
learn= iris[-ix,]
test = iris[ix,]

mod = glm(Species~., data=learn, family = "binomial")

Warning messages:
1: glm.fit: algorithm did not converge
2: glm.fit: fitted probabilities numerically 0 or 1 occurred

pred_ls = predict(mod, newdata = learn, type = "response")
pred_ts = predict(mod, newdata = test, type = "response")

perf_ts = ROCR::prediction(pred_ts, test$Species)
auc_ts = ROCR::performance(perf_ts, "aucpr")
plot(auc_ts)

Error: Performance object cannot be plotted. Length of x and y values does not match.

unlist([e-mail][[1]])

[1] 1

perf_ls = ROCR::prediction(pred_ls, learn$Species)
auc_ls = ROCR::performance(perf_ls, "aucpr")
plot(auc_ls)

Error: Performance object cannot be plotted. Length of x and y values does not match.

unlist([e-mail])

[1] 1

[Felix Ernst]

I am not sure, that I understand your question.

You can only plot objects, which have a measure and a x.measure, so this works:

auc_ts = ROCR::performance(perf_ts, "tpr", "fpr")
plot(auc_ts)

The result of your example [e-mail] also reports the correct values, since the prediction is perfect.

I suggest you read the manual ?performance in more detail. It clearly states:

aucpr:
Area under the Precision/Recall curve. Since the output of aucpr is cutoff-independent, this measure cannot be combined with other measures into a parametric curve.

So this is behavior is expected.