# ROC: Estimation of the traditional ROC curves (without censoring) In ROC632: Construction of diagnostic or prognostic scoring system and internal validation of its discriminative capacities based on ROC curve and 0.633+ boostrap resampling.

## Description

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

## Usage

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

## Arguments

 `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.

## Details

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

## Value

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.

## Author(s)

Y. Foucher <[email protected]>

## Examples

 ```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) ```

ROC632 documentation built on May 30, 2017, 7:34 a.m.