BSGS (version 2.0)

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

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

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)