Best Cutpoint for a ROC Curve

Returns the best cutpoint for a given classification model.

Usage

BestCut(x, method = c("youden", "closest.topleft"))

Arguments

x: a roc object from the roc function
method: one of "youden" or "closest.topleft", controls how the optimal threshold is determined. See details.

Details

The method argument controls how the optimal threshold is determined.

'youden'

Youden's J statistic (Youden, 1950) is employed. The optimal cut-off is the threshold that maximizes the distance to the identity (diagonal) line. Can be shortened to “y”.

The optimality criterion is: $$max(sensitivities + specificities)$$

'closest.topleft'

The optimal threshold is the point closest to the top-left part of the plot with perfect sensitivity or specificity. Can be shortened to “c” or “t”.

The optimality criterion is: $$min((1 - sensitivities)^2 + (1- specificities)^2)$$

Value

the threshold value

References

Xavier Robin, Natacha Turck, Alexandre Hainard, et al. (2011) “pROC: an open-source package for R and S+ to analyze and compare ROC curves”. BMC Bioinformatics, 7, 77. doi:10.1186/1471-2105-12-77 .

Author

Robin Xavier <pROC-cran@xavier.robin.name>, Andri Signorell <andri@signorell.net> (interface)

Examples

r.glm <- FitMod(diabetes ~ ., data = d.pima, fitfn="logit")

ROC(r.glm)
#> Setting levels: control = neg, case = pos
#> Setting direction: controls < cases
#> 
#> Call:
#> roc.default(response = Response(x), predictor = predict(x, type = "prob")[,     2], plot = FALSE)
#> 
#> Data: predict(x, type = "prob")[, 2] in 500 controls (Response(x) neg) < 268 cases (Response(x) pos).
#> Area under the curve: 0.8394
BestCut(ROC(r.glm))
#> Setting levels: control = neg, case = pos
#> Setting direction: controls < cases
#>   threshold specificity sensitivity 
#>   0.3536825   0.7840000   0.7388060