R: Simulate Data

sim_data {dlim}

R Documentation

Simulate Data

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

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`")