validate: Validate a sample size analysis

Description

This function can be used to validate the recommendation obtained from a sample size analysis.

Usage

validate(
  method,
  replications = 3000,
  cores = NULL,
  backend_type = NULL,
  verbose = TRUE
)

Value

An R6::R6Class() instance of Validation class that contains the results of the validation.

Main fields:

$sample: The sample size used for the validation.
$measures: The performance measures observed during validation.
$statistic: The statistic computed on the performance measures.
$percentile_value: The performance measure value at the desired percentile.
$validator: An R6::R6Class() instance of StepOne class.

The plot S3 method can be called on the return value to visualize the validation results (i.e., see plot.Validation()).

plot(validation)

Arguments

method: An object of class Method produced by running powerly().
replications: A single positive integer representing the number of Monte Carlo simulations to perform for the recommended sample size. The default is 1000. Whenever possible, a value of 10000 should be preferred for a higher accuracy of the validation results.
cores: A single positive positive integer representing the number of cores to use for running the validation in parallel, or NULL. If NULL (the default) the validation will run sequentially.
backend_type: A character string indicating the type of cluster to create for running the validation in parallel, or NULL. Possible values are "psock" and "fork". If NULL the backend is determined based on the computer architecture (i.e., fork for Unix and MacOS and psock for Windows).
verbose: A logical value indicating whether information about the status of the validation should be printed while running. The default is TRUE.

Details

The sample sizes used during the validation procedure is automatically extracted from the method argument.

Examples

Run this code

# Perform a sample size analysis.
results <- powerly(
    range_lower = 300,
    range_upper = 1000,
    samples = 30,
    replications = 30,
    measure = "sen",
    statistic = "power",
    measure_value = .6,
    statistic_value = .8,
    model = "ggm",
    nodes = 10,
    density = .4,
    cores = 2,
    verbose = TRUE
)

# Validate the recommendation obtained during the analysis.
validation <- validate(results, cores = 2)

# Plot the validation results.
plot(validation)

# To see a summary of the validation procedure, we can use the `summary` S3 method.
summary(validation)