generate_errors: Helper function to generate simulated errors.

In Yu-Group/dgpoix: Generate synthetic data that is as fresh as the real thing

Helper function to generate simulated errors.

Description

Generate simulated errors from the specified error function and passed arguments.

Usage

generate_errors(err = NULL, n, X, ...)

Arguments

err	Function from which to generate simulate error vector. Default is NULL which returns an error vector of all zeros.
n	Number of samples.
X	Data matrix or data frame. Used to determine n if n is missing.
...	Other arguments to pass to err() to generate the error vector.

Details

The arguments n and X (if provided) are automatically passed to the function err under arguments of the same name. Note however that they may be unused arguments if err does not take in arguments named n and/or X as input.

Value

A vector of simulated errors with length n.

Examples

# generate standard Gaussian error vector of length 150
errs <- generate_errors(err = rnorm, n = 150)
# or alternatively,
errs <- generate_errors(err = rnorm, X = iris)

# generate Gaussian error vector with mean 0 and sd 2
errs <- generate_errors(err = rnorm, n = 150, sd = 2)

# generate error vector of all 0s
errs <- generate_errors(err = NULL, n = 150)

# generate error vector from custom error function
err_fun <- function(n, rho) {
  # simulate correlated errors from a autoregressive-1 Gaussian process
  row1 <- rho^(0:(n - 1))
  Sigma <- stats::toeplitz(row1)
  return(MASS::mvrnorm(1, mu = rep(0, n), Sigma = Sigma))
} 
errs <- generate_errors(err = err_fun, n = 100, rho = 0.75)

Yu-Group/dgpoix documentation built on June 3, 2022, 1:40 a.m.