Plot of ROC curves estimated under parametric model assumptions on the continuous diagnostic marker.

1 | ```
ROC.plot(ydat, xdat, distr = "exp", method = "RPstar", mc = 1)
``` |

`ydat` |
data vector of the diagnostic marker measurements on the sample of non-diseased individuals (from Y). |

`xdat` |
data vector of the diagnostic marker measurements on the sample of diseased individuals (from X). |

`distr` |
character string specifying the type of distribution assumed for Y and X. Possible choices for |

`method` |
character string specifying the methodological approach used for estimating the
probability R, which is here interpreted as the area under the ROC curve (AUC).
The argument |

`mc` |
a numeric value indicating single or multiple plots in the same figure.
In case |

If `mc`

is different from 1, `method`

does not need to be specified.

Plot of ROC curves

The two independent random variables Y and X with given distribution
`distr`

are measurements of the diagnostic marker on the diseased
and non-diseased subjects, respectively.

In "Wald" method, or equivalently "RP" method, MLEs for parameters of the Y and X distributions
are computed and then used to estimate specificity and sensitivity.
These measures are evaluated as *P(Y<t)* and *P(X>t)*, respectively.

In "RPstar" method, parameters of the Y and X distributions are estimated
from the *r_p^**-based estimate of the AUC.

Giuliana Cortese

Cortese G., Ventura L. (2013). Accurate higher-order likelihood inference on *P(Y<X)*. Computational Statistics, 28:1035-1059.

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