validate: Validate a sample size analysis
In powerly: Sample Size Analysis for Psychological Networks and More

validate

R Documentation

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
)

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.

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)

Examples

# 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)

powerly documentation built on Sept. 9, 2022, 5:07 p.m.