rocsum {dtComb}

Generate ROC curves and related statistics for the given markers and Combination score.

Description

The rocsum function returns the ROC curves with coordinates, Area Under the Curves of markers and combination score, Area Under the Curve comparison of markers and combination score, Confusion matrices for both markers and combination score with the cutoff values derived from the ROC Curves.

Usage

rocsum(
  markers = NULL,
  comb.score = NULL,
  status = NULL,
  event = NULL,
  direction = c("auto", "<", ">"),
  conf.level = 0.95,
  cutoff.method = c("CB", "MCT", "MinValueSp", "MinValueSe", "ValueSp", "ValueSe",
    "MinValueSpSe", "MaxSp", "MaxSe", "MaxSpSe", "MaxProdSpSe", "ROC01", "SpEqualSe",
    "Youden", "MaxEfficiency", "Minimax", "MaxDOR", "MaxKappa", "MinValueNPV",
    "MinValuePPV", "ValueNPV", "ValuePPV", "MinValueNPVPPV", "PROC01", "NPVEqualPPV",
    "MaxNPVPPV", "MaxSumNPVPPV", "MaxProdNPVPPV", "ValueDLR.Negative",
    "ValueDLR.Positive", "MinPvalue", "ObservedPrev", "MeanPrev", "PrevalenceMatching"),
  show.plot = show.plot
)

Arguments

markers

a numeric data frame that includes two diagnostic tests results

comb.score

a matrix of numeric combination scores calculated according to the given method

status

a factor vector that includes the actual disease status of the patients

event

a character string that indicates the event in the status to be considered as positive event

direction

a character string determines in which direction the comparison will be made. “>”: if the predictor values for the control group are higher than the values of the case group (controls > cases). “<”: if the predictor values for the control group are lower or equal than the values of the case group (controls < cases).

conf.level

a numeric values determines the confidens interval for the ROC curve(0.95, default).

cutoff.method

a character string determines the cutoff method for the ROC curve.

show.plot

a logical. If TRUE, a ROC curve is plotted. Default is FALSE.

Value

A list of numeric ROC Curves, AUC statistics and Confusion matrices.

Author(s)

Serra Ilayda Yerlitas, Serra Bersan Gengec, Necla Kochan, Gozde Erturk Zararsiz, Selcuk Korkmaz, Gokmen Zararsiz