Title:	Variable Selection using Shrinkage Priors
Version:	1.0.0
Description:	Bayesian variable selection using shrinkage priors to identify significant variables in high-dimensional datasets. The package includes methods for determining the number of significant variables through innovative clustering techniques of posterior distributions, specifically utilizing the 2-Means and Sequential 2-Means (S2M) approaches. The package aims to simplify the variable selection process with minimal tuning required in statistical analysis.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.1
Imports:	bayesreg, stats
Suggests:	covr, MASS, knitr, rmarkdown, tinytex, testthat (≥ 3.0.0)
Config/testthat/edition:	3
Maintainer:	Nilson Chapagain <nilson.chapagain@gmail.com>
URL:	https://github.com/nilson01/VsusP-variable-selection-using-shrinkage-priors
BugReports:	https://github.com/nilson01/VsusP-variable-selection-using-shrinkage-priors/issues
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2024-06-24 08:16:11 UTC; nilson
Author:	Nilson Chapagain [aut, cre], Debdeep Pati [aut]
Repository:	CRAN
Date/Publication:	2024-06-25 14:10:02 UTC

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

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)

Variable selection using shrinkage priors :: S2MVarSelection

Description

S2MVarSelection function will take S2M: a list obtained from the 2Means.variables function and H: the estimated number of signals obtained from the optimal.H.b.i function. This will give out the important subset of variables for the Gaussian Linear model.

Usage

S2MVarSelection(Beta, H = 5)

Arguments

Value

a vector containing indices of important subset of variables of dimension H X 1.

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)
# Fit a model using gaussian horseshoe+ for 200 samples
# # recommended n.samples is 5000 and burning is 2000
rv.hs <- bayesreg::bayesreg(Y ~ ., df, "gaussian", "horseshoe+", 110, 100)

Beta <- rv.hs$beta
H <- 3
impVariablesGLM <- S2MVarSelection(Beta, H)
impVariablesGLM

Variable selection using shrinkage priors :: S2MVarSelectionV1

Description

S2MVarSelectionV1 function will take S2M: a list obtained from the 2Means.variables function and H: the estimated number of signals obtained from the optimal.b.i function. This will give out the important subset of variables for the Gaussian Linear model.

Usage

S2MVarSelectionV1(S2M, H = 5)

Arguments

Value

a vector of indices of important subset of variables for the Gaussian Linear modelof shape H X 1

Variable selection using shrinkage priors :: Sequential2Means

Description

Sequential2Means function will take as input X: design matrix, Y : response vector, t: vector of tuning parameter values from Sequential 2-means (S2M) variable selection algorithm. 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

Sequential2Means(
  X,
  Y,
  b.i,
  prior = "horseshoe+",
  n.samples = 5000,
  burnin = 2000
)

Arguments

Value

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

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))
b.i <- seq(0, 1, 0.05)

# Sequential2Means with horseshoe+ using gibbs sampling
# recommended n.samples is 5000 and burning is 2000
S2M <- Sequential2Means(X, Y, b.i, "horseshoe+", 110, 100)
Beta <- S2M$Beta
H.b.i <- S2M$H.b.i

# -----------------------------------------------------------------
# Example 2: Gaussian Model and ridge 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))
b.i <- seq(0, 1, 0.05)
# Sequential2Means with ridge regression using gibbs sampling
# recommended n.samples is 5000 and burning is 2000
S2M <- Sequential2Means(X, Y, b.i, "ridge", 110, 100)
Beta <- S2M$Beta
H.b.i <- S2M$H.b.i

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

Value

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

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

Variable selection using shrinkage priors :: numNoiseCoeff

Description

Variable selection using shrinkage priors :: numNoiseCoeff

Usage

numNoiseCoeff(Beta.i, b.i_r)

Arguments

Value

number of noise coefficients of numeric data type