Sim_data_test

A test set of outcomes and predictor variables to be used with <code>Sim_data</code>.

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_test function

A data frame with 400 observations and the following objects:<dl>
 <dt><code>Y_test</code></dt>
<dd>Outcome variable of length \(400\) for test set.</dd> <dt><code>Z_test</code></dt>
<dd>A \(400 \times 400\) matrix of binary predictor variables for test set.</dd> 
</dl>

Format

Simulated high-dimensional test data set for sparse linear regression — Sim_data_test

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

 <dt><code>Z_test</code></dt>
<dd>A \(400 \times 400\) matrix of binary predictor variables for test set.</dd>

 
</dl>

Sim_data_test: Simulated high-dimensional test data set for sparse linear regression

Description

Usage

Arguments

Format

Examples