Use Simulated Data to Test the Power of Your Research Design — simulate_design

Uses your simulation code, a vector of potential sample sizes and your a priori assumption of the effect size to estimate the power of your research design.

Usage

simulate_design_power(
  d,
  protocol,
  input_sim_func,
  range_n,
  effect_size,
  runs = 100,
  input_sim_params = NULL
)

Arguments

d: A character vector of the research design steps function names
protocol: A list of choices for the steps of the design
input_sim_func: A function that, when called, will return a simulated input to the first step for the function. See Details section for further information.
range_n: A vector containing the differnt sample sizes for which you wan to estimate the power of your design.
effect_size: The effect size that you want your simulated data to reflect.
runs: How many runs do you want to simulate to estimate you power?
input_sim_params: If your input_sim_func needs additional parameters besides n and effect_size you can provide a list of them here.

Value

A data frame containing the output of the last step of the design

along with the simulation parameters,

Details

The function provided via input_sim_func needs to take a sample size parameter and an effect size parameter as the first two input parameters. See the vignette of the package for additional information on how to implement the RDF analysis workfow.

Examples

if (FALSE) {
  print("Sorry. No examples yet.")
}