| cgAUC-package {cgAUC} | R Documentation |
Calculate AUC when gold standard is continuous with large variables.
Description
In this package, the cgAUC is used to calculate the AUC-type measure raised in Obuchowski(2006) when gold standard is continuous.
Details
| Package: | cgAUC |
| Type: | Package |
| Version: | 1.2.1 |
| Date: | 2014-08-24 |
| License: | GPL (>=2) |
Author(s)
Yuan-chin I. Chang, Yu-chia Chang, and Ling-wan Chen
Maintainer: Yu-chia Chang <curare7177@gmail.com>
References
Chang, YCI. Maximizing an ROC type measure via linear combination of markers when the gold reference is continuous. Statistics in Medicine 2012.
Obuchowski NA. An ROC-type measure of diagnostic accuracy when the gold standard is continuous-scale. Statistics in Medicine 2006; 25:481–493.
Obuchowski N. Estimating and comparing diagnostic tests accuracy when the gold standard is not binary. Statistics in Medicine 2005; 20:3261–3278.
Friedman JH, Popescu BE. Gradient directed regularization for linear regression and classification. Technical Report, Department of Statistics, Stanford University, 2004.
Examples
# n = 100; p = 5;
# r.x = matrix(rnorm(n * p), , p) # raw data
# r.z = r.x[ ,1] + rnorm(n) # gold standard
# x = scale(r.x) # standardized of raw data
# z = scale(r.z) # standardized of gold standard
# h = n^(-1 / 2)
# t1 = cgAUC(r.x, r.z, h, delta = 1, auto = FALSE, tau = 1, scale = 1) # the delta be constant
# t1
# t2 = cgAUC(r.x, r.z, h, delta = 1, auto = TRUE, tau = 1, scale = 1) # the delta be variable
# t2