Models have high accuracy but very low AUC PR curve

Question

I'm doing balanced (70%-30%) binary classification (yes/no) I'm trying to combine caret train objects with PRROC's pr.curve function. I'm using a confusion matrix to determine which class is labelled as "positive" to make sure I put the right class in the score.class0 and score.class1 arguments.

On my model, I have a high accuracy, recall and specificity as read in the confusion matrix. However, the result of the AUC from the prcurve function is extremely low.

here's a reprex of how my model is constructed, with more "predictable" results using naive bayes, but the AUC PRcurve results are much much lower for all the models I'm using (glm, knn, class.tree,...)

library(caret)
library(PRROC)

set.seed(73, sample.kind = "Rounding")

sonar_index <- createDataPartition(Sonar$Class, times = 1, p= 0.2, list = F)
sonar_train <- Sonar[-sonar_index,]
sonar_test <- Sonar[sonar_index,]

ctrl <- trainControl(method="cv", summaryFunction=twoClassSummary, classProbs=T,
                         savePredictions = T)

nb <- train(Class ~ V17 + V13,
                data = sonar_train, method = "nb", trControl = ctrl)

cm <- confusionMatrix(predict(nb, sonar_test), sonar_test$Class)

plot(pr.curve(scores.class0 = nb$pred$M[sonar_test$Class == "M"], 
              scores.class1 = nb$pred$R[sonar_test$Class == "R"],
              curve = T))