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 nondiseased 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 nondiseased 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 higherorder likelihood inference on P(Y<X). Computational Statistics, 28:10351059.
Prob
1 2 3 4 5 
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