Simulate Data

Simulate data to use with the dlim package. There are different effect modification scenarios to choose for simulation.

Usage

sim_data(
  x,
  L = NULL,
  modifiers,
  noise = 1,
  type = 2,
  SNR,
  ncovariates = 0,
  gamma = 1
)

x: a time series vector of length n or matrix of lagged exposures for n individuals (class "numeric", "matrix")
L: a vector of length 1 containing the number of lag terms. This is required if x is vector, and is not used if x is a matrix (class "numeric")
modifiers: vector of length n containing modifying values (class "numeric")
noise: a vector of length 1 containing the standard deviation for a normal distribution with mean 0 used to add noise to the simulated response values. Must proivde if SNR is not provided (class "numeric")
type: a vector containing the number 1, 2, 3, or 4 for simulation modification type: none, linear, non-linear shift, non-linear shift with linear scale (class "numeric")
SNR: The signal-to-noise ratio. If SNR is provided, but noise is not, noise is reset to be the standard deviation of the response, before adding noise. (class "numeric")
ncovariates: number of covariates to add to the model, numeric vector of length 1.
gamma: True coefficient for the main effect of the modifier (class "numeric")

This returns a list of 8 items:

x: a lagged exposure matrix. If x was a matrix, it is unchanged. (class "matrix")
L: a numeric vector of length 1 containing the number of lag terms (class "numeric")
modifiers: the modifiers argument (class "numeric")
y: a numeric vector of length nrow(x) containing the perturbed simulated response values. (class "numeric")
betas: a matrix containing true coefficients for each lag/modifier combination, with each row representing a lag and each column a modifier (class "matrix")
betas_cumul: a numeric vector of length L+1 containing cumulative true coefficients for the lag terms, summed over modifiers (class "numeric")
Z: covariates (class "matrix")
gammas: true coefficients for the covariates (class "numeric")