sim_data: Simulate data based on a model and parameter distributions

In ronkeizer/vpc: Create Visual Predictive Checks

Simulate data based on a model and parameter distributions

Description

Simulate data based on a model and parameter distributions

Usage

sim_data(
  design = cbind(id = c(1, 1, 1), idv = c(0, 1, 2)),
  model = function(x) {
     return(x$alpha + x$beta)
 },
  theta,
  omega_mat,
  par_names,
  par_values = NULL,
  draw_iiv = "mvrnorm",
  error = list(proportional = 0, additive = 0, exponential = 0),
  n = 100
)

Arguments

design	a design dataset. See example
model	A function with the first argument the simulation design, i.e. a dataset with the columns ... The second argument to this function is a dataset with parameters for every individual. This can be supplied by the user, or generated by this sim_data if theta and omega_mat are supplied.
theta	vector of fixed effect parameters
omega_mat	vector of between subject random effects, specified as lower triangle
par_names	A character vector linking the parameters in the model to the variables in the dataset. See example.
par_values	parameter values
draw_iiv	draw between subject random effects?
error	see example
n	number of simulations to perform

Details

This function generates the simulated dependent values for use in the VPC plotting function.

Value

a vector of simulated dependent variables (for us in the VPC plotting function)

See Also

vpc

ronkeizer/vpc documentation built on May 11, 2023, 11:09 p.m.