Sequential2MeansBeta {VsusP}

Variable selection using shrinkage prior :: Sequential2MeansBeta

Description

Sequential2MeansBeta function will take as input Beta : N by p matrix consisting of N posterior samples of p variables, lower : the lower bound of the chosen values of the tuning parameter, upper : the upper bound of the chosen values of the tuning parameter, and l :the number of chosen values of the tuning parameter. The function will return a list S2M which will hold p: the total number of variables, b.i: the values of the tuning parameter, H.b.i : the estimated number of signals corresponding to each b.i, abs.post.median: medians of the absolute values of the posterior samples.

Usage

Sequential2MeansBeta(Beta, lower, upper, l)

Arguments

Beta

N by p matrix consisting of N posterior samples of p variables

lower

the lower bound of the chosen values of the tuning parameter of numeric data type.

upper

the upper bound of the chosen values of the tuning parameter of numeric data type.

l

the number of chosen values of the tuning parameter of numeric data type.

Value

A list S2M which will hold p, b.i, and H.b.i:

p

total number of variables in the model

b.i

the vector values of the tuning parameter specified by the user

H.b.i

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

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

# -----------------------------------------------------------------
# Example 1: Gaussian Model and Horseshoe prior

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)

# beta samples for gaussian model using horseshow prior and gibbs sampling
rv.hs <- bayesreg::bayesreg(Y ~ ., df, "gaussian", "horseshoe+", 110, 100)

Beta <- t(rv.hs$beta)
lower <- 0
upper <- 1
l <- 20
S2Mbeta <- Sequential2MeansBeta(Beta, lower, upper, l)
H.b.i <- S2Mbeta$H.b.i

# -----------------------------------------------------------------
# Example 2: normal model and lasso prior

#' 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, "normal", "lasso", 150, 100)

Beta <- t(rv.hs$beta)
lower <- 0
upper <- 1
l <- 15
S2Mbeta <- Sequential2MeansBeta(Beta, lower, upper, l)
H.b.i <- S2Mbeta$H.b.i