Estimated ROC curves
Plot of ROC curves estimated under parametric model assumptions on the continuous diagnostic marker.
ROC.plot(ydat, xdat, distr = "exp", method = "RPstar", mc = 1)
data vector of the diagnostic marker measurements on the sample of non-diseased individuals (from Y).
data vector of the diagnostic marker measurements on the sample of diseased individuals (from X).
character string specifying the type of distribution assumed for Y and X. Possible choices for
character string specifying the methodological approach used for estimating the
probability R, which is here interpreted as the area under the ROC curve (AUC).
a numeric value indicating single or multiple plots in the same figure.
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.
Cortese G., Ventura L. (2013). Accurate higher-order likelihood inference on P(Y<X). Computational Statistics, 28:1035-1059.
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