Compute confidence intervals and point estimates for the probability R, under parametric model assumptions for Y and X. Y and X are two independent continuous random variable from two different populations.

1 | ```
Prob(ydat, xdat, distr = "exp", method = "RPstar", level = 0.05)
``` |

`ydat` |
data vector of the sample measurements from Y. |

`xdat` |
data vector of the sample measurements 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 inference (confidence intervals and point estimates) on the AUC.
The argument |

`level` |
it is the |

`PROB` |
Point estimate of |

`C.Interval` |
Confidence interval of R at confidence level |

Giuliana Cortese

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

1 2 3 4 5 6 7 8 9 | ```
# data from the first population
Y <- rnorm(15, mean=5, sd=1)
# data from the second population
X <- rnorm(10, mean=7, sd=1.5)
level <- 0.01 ## \eqn{\alpha} level
# estimate and confidence interval under the assumption of two
# normal variables with different variances.
Prob(Y, X, "norm_DV", "RPstar", level)
# method has to be set equal to "RPstar".
``` |

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