Last data update: 2014.03.03

 R: Evaluate TCR, TPR and FPR for variable selection problems.
TCR.TPR.FPR.CGS.SMPR Documentation

Evaluate TCR, TPR and FPR for variable selection problems.

Description

Calculate the true classification rate (TCR), the true positive rate (TPR), and the false positive rate (FPR).

Usage

TCR.TPR.FPR.CGS.SMP(Output, True.r, Critical.Point)

Arguments

Output

A list of random samples for parameters.

True.r

The true value of indicator variable.

Critical.Point

When the posterior estiamte of r=1 greater than this critical point, then it would be assign to 1, and otherwise 0.

Value

A list is returned with TCP, TPR and FPR.

Examples

## Not run: 
output = BSGS.Simple.SaveAllSimulatedSamples(Y, X, Group.Index, r.value, eta.value, 
	     beta.value, tau2.value, rho.value, theta.value, sigma2.value, nu, lambda, 
	     Num.of.Iter.Inside.CompWise, Num.Of.Iteration, MCSE.Sigma2.Given)
TCR.TPR.FPR.BSGS(output, r.true, critical.value)

## End(Not run)

Results


Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and IMSBIO