simRegression: Simulate noisy observations from a dynamic regression model

In drkowal/dsp: Dynamic Shrinkage Processes

Simulate noisy observations from a dynamic regression model

Description

Simulates data from a time series regression with dynamic regression coefficients. The dynamic regression coefficients are simulated as a Gaussian random walk, where jumps occur with a pre-specified probability sparsity. The coefficients are initialized by a N(0,1) simulation.

Usage

simRegression(
  T = 200,
  p = 20,
  p_0 = 15,
  sparsity = 0.05,
  RSNR = 5,
  ar1 = 0,
  include_plot = FALSE
)

Arguments

T	number of time points
p	number of predictors (total)
p_0	number of true zero regression terms
sparsity	the probability of a jump (i.e., a change in the dynamic regression coefficient)
RSNR	root-signal-to-noise ratio
ar1	the AR(1) coefficient for the predictors X; default is zero for iid N(0,1) predictors
include_plot	logical; if TRUE, include a plot of the simulated data and the true curve

Value

a list containing

• the simulated function y

• the simulated predictors X

• the simulated dynamic regression coefficients beta_true

• the true function y_true

• the true observation standard devation sigma_true

Note

The root-signal-to-noise ratio is defined as RSNR = [sd of true function]/[sd of noise].

Examples

sims = simRegression() # default simulations
names(sims) # variables included in the list

drkowal/dsp documentation built on July 19, 2023, 11:42 a.m.