rejection_rates: Rejection Rates

In power4mome: Power Analysis for Moderation and Mediation

Rejection Rates

Description

Get all rejection rates of all tests stored in a power4test object or other supported objects.

Usage

rejection_rates(object, ...)

## Default S3 method:
rejection_rates(object, ...)

## S3 method for class 'power4test'
rejection_rates(
  object,
  all_columns = FALSE,
  ci = TRUE,
  level = 0.95,
  se = FALSE,
  collapse = c("none", "all_sig", "at_least_one_sig", "at_least_k_sig"),
  at_least_k = 1,
  ...
)

## S3 method for class 'power4test_by_es'
rejection_rates(
  object,
  all_columns = FALSE,
  ci = TRUE,
  level = 0.95,
  se = FALSE,
  ...
)

## S3 method for class 'power4test_by_n'
rejection_rates(
  object,
  all_columns = FALSE,
  ci = TRUE,
  level = 0.95,
  se = FALSE,
  ...
)

## S3 method for class 'rejection_rates_df'
print(x, digits = 3, annotation = TRUE, abbreviate_col_names = TRUE, ...)

Arguments

object	The object from which the rejection rates are to be computed, such as a power4test object, a power4test_by_n object, or a power4test_by_es object.
...	Optional arguments. For the print method, these arguments will be passed to the print method of data.frame objects print.data.frame(). Not used by other methods.
all_columns	If TRUE, all columns stored by a test will be extracted. Default is FALSE and only essential columns related to power will be printed.
ci	If TRUE, confidence intervals for the rejection rates (column reject or sig) will be computed. Normal approximation is used in forming the confidence intervals.
level	The level of confidence for the confidence intervals, if ci is TRUE. Default is .95, denoting 95%.
se	If TRUE, standard errors for the rejection rates (column reject or sig) will be computed. Normal approximation is used to compute the standard errors.
collapse	Whether a single decision (significant vs. not significant) is made across all tests for a test that consists of several tests (e.g., the tests of several parameters). If "none", tests will be summarized individually. If "all_sig", then the set of tests is considered significant if all individual tests are significant. If "at_least_one_sig", then the set of tests is considered significant if at least one of the tests is significant. If "at_least_k_sig", then the set of tests is considered significant if at least k tests are significant, k set by the argument at_least_k.
at_least_k	Used by collapse, the number of tests required to be significant for the set of tests to be considered significant.
x	The rejection_rates_df object to be printed.
digits	The number of digits to be printed after the decimal.
annotation	Logical. Whether additional notes will be printed.
abbreviate_col_names	Logical. Whether some column names will be abbreviated.

Details

For a power4test object, rejection_rates loops over the tests stored in a power4test object and retrieves the rejection rate of each test.

The rejection_rates method for power4test_by_es objects is used to compute the rejection rates from a power4test_by_es object, with effect sizes added to the output.

The rejection_rates method for power4test_by_n objects is used to compute the rejection rates, with sample sizes added to the output.

Value

The rejection_rates method returns a rejection_rates_df object, with a print method.

If the input (object) is a power4test object, the rejection_rates_df object is a data-frame like object with the number of rows equal to the number of tests. Note that some tests, such as the test by test_parameters(), conduct one test for each parameter. Each such test is counted as one test.

The data frame has at least these columns:

• test: The name of the test.

• label: The label for each test, or "Test" if a test only does one test (e.g., test_indirect_effect()).

• pvalid: The proportion of valid tests across all replications.

• reject: The rejection rate for each test. If the null hypothesis is false, then this is the power.

The rejection_rates method for power4test_by_es objects returns an object of the class rejection_rates_df_by_es, which is a subclass of rejection_rates_df. It is a data frame which is similar to the output of rejection_rates(), with two columns added for the effect size (pop_es_name and pop_es_values) for each test.

The rejection_rates method for power4test_by_n objects returns an object of the class rejection_rates_df_by_n, which is a subclass of rejection_rates_df. It is a data frame which is similar to the output of a power4test object, with a column n added for the sample size for each test.

The print method of a rejection_rates_df object returns the object invisibly. It is called for its side-effect.

See Also

power4test(), power4test_by_n(), and power4test_by_es(), which are supported by this method.

Examples

# Specify the population model

model_simple_med <-
"
m ~ x
y ~ m + x
"

# Specify the effect sizes (population parameter values)

model_simple_med_es <-
"
y ~ m: l
m ~ x: m
y ~ x: n
"

# Generate some datasets to check the model

sim_only <- power4test(nrep = 4,
                       model = model_simple_med,
                       pop_es = model_simple_med_es,
                       n = 100,
                       R = 50,
                       ci_type = "boot",
                       fit_model_args = list(fit_function = "lm"),
                       do_the_test = FALSE,
                       iseed = 1234)

# Do the test 'test_indirect_effect' on each datasets

test_out <- power4test(object = sim_only,
                       test_fun = test_indirect_effect,
                       test_args = list(x = "x",
                                        m = "m",
                                        y = "y",
                                        boot_ci = TRUE,
                                        mc_ci = FALSE))

# Do the test 'test_parameters' on each datasets
# and add the results to 'test_out'

test_out <- power4test(object = test_out,
                       test_fun = test_parameters)

# Compute and print the rejection rates for stored tests

rejection_rates(test_out)

# See the help pages of power4test_by_n() and power4test_by_es()
# for other examples.

power4mome documentation built on Sept. 9, 2025, 5:35 p.m.