Simulate data based on a model and parameter distributions

Source: R/sim_data.R

Simulate data based on a model and parameter distributions

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

Value

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

Details

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

See also

vpc