add roc curve

DESCRIPTION

@@ -10,9 +10,11 @@ Depends: R (>= 3.4.0)
 Imports:
     nlme,
     RANN,
+    pROC,
     dsBase
 AggregateMethods:
     rilmDS.b,
+    rocDS,
     tTestFDS2
 AssignMethods:
     dataFrameFillDS

R/rocDS.R

@@ -0,0 +1,38 @@
+#'
+#' @title Computes the important points of a roc curve for given reference/prediction
+#' @description Calculates roc points.
+#' @details if the length of prediction and reference vectors don't match an error is returned.
+#' @param prediction a numeric vector (values between 0 and 1) holding the tendency to lean towards a positive or negative outcome.
+#' @param reference a logical (boolean) vector refering to the "truth" on which case is positive/true or negative/false.
+#' @return two same-length numerical vectors corresponding to the sensitivity/specificity values to draw a roc curve.
+#' @author Matthis A
+#' @export
+#'
+rocDS <- function(reference, prediction){
+
+  #############################################################
+  # MODULE 1: CAPTURE THE nfilter SETTINGS
+  # thr <- listDisclosureSettingsDS()
+  # nfilter.tab <- as.numeric(thr$nfilter.tab)
+  #nfilter.glm <- as.numeric(thr$nfilter.glm)
+  #nfilter.subset <- as.numeric(thr$nfilter.subset)
+  #nfilter.string <- as.numeric(thr$nfilter.string)
+  #############################################################
+
+  res <- pROC::roc(reference, prediction)
+
+  out.specificities <- res$specificities
+  out.sensitivities <- res$sensitivities
+
+  nex <- Position(function(x) x>0.5, res$thresholds)
+  pre <- nex - 1
+  s <- (res$thresholds[nex]-0.5)/(res$thresholds[nex]-res$thresholds[pre])
+  out.tspec <- s*res$specificities[nex]+(1-s)*res$specificities[pre]
+  out.tsens <- s*res$sensitivities[nex]+(1-s)*res$sensitivities[pre]
+
+  out.obj <- list(Specificities=out.specificities,Sensitivities=out.sensitivities, Threshold=c(x=out.tspec, y=out.tsens))
+  return(out.obj)
+
+}
+#AGGREGATE FUNCTION
+# rocDS