Build a ROC curve

This is a wrapper to the main function pROC of the pROC package (by Xavier Robin et al.). It builds a ROC curve and returns a "roc" object, a list of class "roc".

Usage

ROC(x, resp = NULL, ...)

Arguments

x: a model object, or the predicted probabilities, when resp is not NULL.
resp: the response
...: all arguments are passed to roc().

Details

Partial ROC is calculated following Peterson et al. (2008; doi:10.1016/j.ecolmodel.2007.11.008 ). This function is a modification of the PartialROC funcion, available at https://github.com/narayanibarve/ENMGadgets.

Value

A data.frame containing the AUC values and AUC ratios calculated for each iteration.

Author

Andri Signorell <andri@signorell.net>

References

Peterson, A.T. et al. (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Modell., 213, 63-72.

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

# plot ROC curves for a list of models
r.rp <- FitMod(diabetes ~ ., data = d.pima, fitfn="rpart")

# combine models to a list
mlst <- list(r.glm, r.rp)

# do the plot
for(i in seq_along(mlst))
  if(i==1){
    plot(ROC(mlst[[i]], grid=TRUE, col=c(hred, hblue)[i]))
  } else {
    lines(ROC(mlst[[i]], col=c(hred, hblue)[i]))
  }
#> Setting levels: control = neg, case = pos
#> Setting direction: controls < cases
#> Setting levels: control = neg, case = pos
#> Setting direction: controls < cases