c_cntin: c_cntin

In cgAUC: Calculate AUC-type measure when gold standard is continuous and the corresponding optimal linear combination of variables with respect to it.

Description

Continue function, when variable was continue.

Usage

c_cntin(y, z, l, h)

Arguments

y	The potential variables. It is a matrix with column of values of a variables. It should be standardized in this application.
z	The gold standard variable. It should be standardized.
l	Linear combination. A vector.
h	The value of h falls into (n^(-1/2), n^(-1/5)).

Value

theta.sh.h.p	The estimate of the theta of Chang(2012).
var	The variance of estimate of the theta of Chang(2012).

Author(s)

Yu-chia Chang

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

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function(y, z, l, h) {
    .Call('cgAUC_c_cntin', PACKAGE = 'cgAUC', y, z, l, h)
}

Example output

function (y, z, l, h) 
{
    .Call("cgAUC_c_cntin", PACKAGE = "cgAUC", y, z, l, h)
}

cgAUC documentation built on May 1, 2019, 10:11 p.m.