Sim_data_cov: Simulated high-dimensional data set for sparse linear...

In probe: Sparse High-Dimensional Linear Regression with PROBE

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

Description

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.

Usage

data("Sim_data_cov")

Format

A data frame with 400 observations and the following objects:

Y

Outcome variable of length 400.

Z

A dataframe of a continuous (Cont_cov) and binary (Binary_cov) covariate.

X

A 400 \times 10000 matrix of binary predictor variables.

beta_tr

The true values of all 10000 regression coefficients.

beta_Z_tr

The true values of the intercept, Cont_cov, and Binary_cov.

signal

The locations of the non-zero regression coefficients.

Examples

data(Sim_data_cov)
str(Sim_data_cov)

probe documentation built on Nov. 2, 2023, 5:49 p.m.