rocsurf.trin: Trinormal ROC surface plot

In trinROC: Statistical Tests for Assessing Trinormal ROC Data

Trinormal ROC surface plot

Description

Function for computation of the trinormal ROC surface.

Usage

rocsurf.trin(x, y, z, p = 300, plot = TRUE, saveVUS = FALSE)

Arguments

x, y, z	Vectors containing the data of the three classes "healthy", "intermediate" and "diseased".
p	An integer for the precision of the surface. p gives the number of gridpoints per axis.
plot	logical. If TRUE (default), the VUS is plotted using surface3d from the package rgl.
saveVUS	A logical whether to save a PNG of the VUS in your current working directory (default is FALSE).

Details

This function takes three-class ROC data and computes the three dimentional surface using the R-package rgl. The ROC surface is defined as

z = ROCs(t_-,t_+) = F_0(c_+) - F_0(c_-)=F_0(G_+^{-1}(t_+) ) -F_0(F_-^{-1}(t_-) ),

where c_-, c_+ are the two cut-off points and F_-, F_0, F_+ the cdf of the three classes with G = 1-F.

Value

A list with the following components:

t1, t2	The vectors t_-=F_-^{(c_-)} and t_+=F_+^{(c_+)}
zVUS	The matix containing the surface values.
x, y, z	The original data.

References

Xiong, C., G. Van Belle, et al. (2006). Measuring and estimating diagnostic accuracy when there are three ordinal diagnostic groups. Statistics in Medicine 25(7), 1251–1273.

Examples

data(cancer)
x1 <- with(cancer, cancer[trueClass=="healthy", 8])
y1 <- with(cancer, cancer[trueClass=="intermediate", 8])
z1 <- with(cancer, cancer[trueClass=="diseased", 8])

rocsurf.trin(x1, y1, z1)

trinROC documentation built on Oct. 29, 2022, 1:12 a.m.