ROC.plot | R Documentation |
Plot of ROC curves estimated under parametric model assumptions on the continuous diagnostic marker.
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.
Prob
# data from the non-diseased population Y <- rnorm(15, mean=5, sd=1) # data from the diseased population X <- rnorm(10, mean=7, sd=1.5) ROC.plot(Y, X, "norm_DV", method = "RP", mc = 2)
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