TCR.TPR.FPR.BSGS: Evaluate TCR, TPR and FPR for sparse group variable selection...

In BSGS: Bayesian Sparse Group Selection

Description

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

Usage

TCR.TPR.FPR.BSGS(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)

BSGS documentation built on May 2, 2019, 4:21 a.m.