perfcurve() is meant for computing performance curves for your model - such as PR curves or ROC curves. In the literature, you may find these curves used for a more rigorous examination of a model's performance than can be given by a single score.
I recommend looking up the doc linked below. The Algorithms > Pointwise Confidence Bounds section explains what goes on under-the-hood after you call the function with your particular arguments. It samples from your input data multiple times, with different thresholds, to come up with a range of P/R scores, which are used to plot the curve.
To calculate a single value, you may extract counts of TP, FP, TN & FN from your labels and predictions; then manually compute -
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
Should be done in 2 lines of code.
Alternatively, you may use confmat() to produce a confusion matrix, to readily give you TP/FP/TN/FN counts. And then do the same computation.
precision = @(confusionMat) diag(confusionMat)./sum(confusionMat,2);
recall = @(confusionMat) diag(confusionMat)./sum(confusionMat,1)';
Hope it Helps!