OptimalHbi {VsusP}

Variable selection using shrinkage priors :: OptimalHbi

Description

OptimalHbi function will take b.i and H.b.i as input which comes from the result of TwoMeans function. It will return plot from which you can infer about H: the optimal value of the tuning parameter.

Usage

OptimalHbi(bi, Hbi)

Arguments

bi

a vector holding the values of the tuning parameter specified by the user

Hbi

The estimated number of signals corresponding to each b.i of numeric data type

Value

the optimal value (numeric) of tuning parameter and the associated H value

References

Makalic, E. & Schmidt, D. F. High-Dimensional Bayesian Regularised Regression with the BayesReg Package arXiv:1611.06649, 2016

Li, H., & Pati, D. Variable selection using shrinkage priors Computational Statistics & Data Analysis, 107, 107-119.

Examples

n <- 10
p <- 5
X <- matrix(rnorm(n * p), n, p)
beta <- exp(rnorm(p))
Y <- as.vector(X %*% beta + rnorm(n, 0, 1))
df <- data.frame(X, Y)
rv.hs <- bayesreg::bayesreg(Y ~ ., df, "gaussian", "horseshoe+", 110, 100)

Beta <- t(rv.hs$beta)
lower <- 0
upper <- 1
l <- 5
S2Mbeta <- Sequential2MeansBeta(Beta, lower, upper, l)

bi <- S2Mbeta$b.i
Hbi <- S2Mbeta$H.b.i
OptimalHbi(bi, Hbi)