Sim_data_cov

This dataset was simulated using a \(100 \times 100\) 2-dimensional setting described in the reference only two covariates are added. The data contains 400 subjects with one outcome, 10000 predictor variables which are to be subjected to the sparsity assumption, and 2 covariates which are not to be subjected to the sparsity assumption.

datasets

Implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. More information can be found in McLain, Zgodic, and Bondell (2022) <arXiv:2209.08139>.

Alexander McLain

probe

Sparse High-Dimensional Linear Regression with PROBE

Anja Zodiac

Sim_data_cov function

A data frame with 400 observations and the following objects:<dl>
 <dt><code>Y</code></dt>
<dd>Outcome variable of length \(400\).</dd> <dt><code>Z</code></dt>
<dd>A dataframe of a continuous (<code>Cont_cov</code>) and binary (<code>Binary_cov</code>) covariate.</dd> <dt><code>X</code></dt>
<dd>A \(400 \times 10000\) matrix of binary predictor variables.</dd> <dt><code>beta_tr</code></dt>
<dd>The true values of all \(10000\) regression coefficients.</dd> <dt><code>beta_Z_tr</code></dt>
<dd>The true values of the intercept, <code>Cont_cov</code>, and <code>Binary_cov</code>.</dd> <dt><code>signal</code></dt>
<dd>The locations of the non-zero regression coefficients.</dd> 
</dl>

Format

Simulated high-dimensional data set for sparse linear regression with non-sparse covariates. — Sim_data_cov

A data frame with 400 observations and the following objects:<dl>
 <dt><code>Y</code></dt>
<dd>Outcome variable of length \(400\).</dd>

 <dt><code>Z</code></dt>
<dd>A dataframe of a continuous (<code>Cont_cov</code>) and binary (<code>Binary_cov</code>) covariate.</dd>

 <dt><code>X</code></dt>
<dd>A \(400 \times 10000\) matrix of binary predictor variables.</dd>

 <dt><code>beta_tr</code></dt>
<dd>The true values of all \(10000\) regression coefficients.</dd>

 <dt><code>beta_Z_tr</code></dt>
<dd>The true values of the intercept, <code>Cont_cov</code>, and <code>Binary_cov</code>.</dd>

 <dt><code>signal</code></dt>
<dd>The locations of the non-zero regression coefficients.</dd>

 
</dl>

Sim_data_cov: Simulated high-dimensional data set for sparse linear regression with non-sparse covariates.

Description

Usage

Arguments

Format

Examples