This function performs estimations of ROC curves (without censoring) according to quantitative marker and a binary outcome.

1 | ```
ROC(status, marker, cut.values)
``` |

`status` |
A numeric vector with the indicators of the disease (e.g. 0=disease-free, 1=disease). |

`marker` |
A numeric vector with the values of the quantitative marker. |

`cut.values` |
The threshold values of the marker for which the coordinates of the ROC are computed. |

This function computes a traditional ROC curve (without right-censoring). The false positive and negative rates are obtained by estimating the corresponding proportion

The function returns a list. `cut.values`

is the vector of the input threshold values. `TP`

and `FP`

represent the corresponding false and true positive rates. `AUC`

is the area under the curve.

Y. Foucher <Yohann.Foucher@univ-nantes.fr>

1 2 3 4 5 6 7 8 | ```
# import and attach the data example
X <- c(1, 2, 3, 4, 5, 6, 7, 8) # The value of the marker
Y <- c(0, 0, 0, 1, 0, 1, 1, 1) # The value of the binary outcome
ROC.obj <- ROC(status=Y, marker=X, cut.values=sort(X))
plot(ROC.obj$FP, ROC.obj$TP, ylab="True Positive Rates",
xlab="False Positive Rates", type="s", lwd=2)
``` |

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