optimal.delta: optimal.delta

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

Description

Find the optimal delta.

Usage

optimal.delta(y, z, l, h, ind.d.l)

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)).
ind.d.l	Void

Value

delta.star

Optimal delta.

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, ind.d.l) 
{
    l.i = matrix(rep(l, times = 50), nrow = 50, byrow = TRUE)
    delta = seq(0, 5, length = 50)
    m = delta %*% t(ind.d.l)
    l.i = l.i + m
    l.i.max = apply(l.i, 1, max)
    l.i = l.i/l.i.max
    theta = rep(0, 50)
    for (i in 2:50) {
        theta[i] = cntin(y, z, l.i[i, ], h)$theta.sh.h.p
    }
    delta.star = delta[which(theta == max(theta))]
    return(delta.star)
  }

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