Power By Effect Sizes

Estimate power for a set of effect sizes (population values of a model parameter).

Usage

power4test_by_es(
  object,
  pop_es_name = NULL,
  pop_es_values = NULL,
  progress = TRUE,
  ...,
  by_seed = NULL,
  by_nrep = NULL,
  save_sim_all = TRUE
)

# S3 method for class 'power4test_by_es'
c(..., sort = TRUE, skip_checking_models = FALSE)

as.power4test_by_es(original_object, pop_es_name)

# S3 method for class 'power4test_by_es'
print(x, print_all = FALSE, digits = 3, ...)

Arguments

object: A power4test object, or a power4test_by_es object. If it is a power4test_by_es object, the first element, which is a power4test object, will be used as the value of this argument.
pop_es_name: The name of the parameter. See the help page of ptable_pop() on the names for the argument pop_es.
pop_es_values: A numeric vector of the population values of the parameter specified in pop_es_names.
progress: Logical. Whether progress of the simulation will be displayed.
...: For power4test_by_es(), they are arguments to be passed to power4test(). For c.power4test_by_es(), they are power4test_by_es() outputs to be combined together. For the print method of the output of power4test_by_es(), they are arguments to be passed to the print method of the output of power4test() (print.power4test()).
by_seed: If set to a number, it will be used to generate the seeds for each call to power4test(). If NULL, the default, then seeds will still be randomly generated but the results cannot be easily reproduced.
by_nrep: If set to a number, it will be used to generate the number of replications (nrep) for each call to power4test(). If set to a numeric vector of the same length as pop_es_values, then these are the nrep values for each of the calls, allowing for different numbers of replications for the population values. If NULL, the default, then the original nrep will be used. This argument is used by x_from_power() for efficiency, and is rarely used when calling this function directly.
save_sim_all: If FALSE, the dataset in the power4test objects are not saved, to reduce the size of the output. Default is TRUE.
sort: WHen combining the objects, whether they will be sorted by population values. Default is TRUE.
skip_checking_models: Whether the check of the data generation model will be checked. Default is TRUE. Should be set to FALSE only when users are certain the they are based on the same model, or when the model is not saved (e.g., save_sim_all set to FALSE when the objects were generated). This argument is used by x_from_power() for efficiency, and is rarely used when calling the c method directly.
original_object: The object to be converted to a power4test_by_es object.
x: The object to be printed.
print_all: If TRUE, all elements in x, that is, the results of all sample sizes examined, will be printed. If FALSE, then only those of the first value of the parameter will be printed.
digits: The numbers of digits displayed after the decimal.

Value

The function power4test_by_es() returns a power4test_by_es object, which is a list of power4test objects, one for each population value of the parameter.

The method c.power4test_by_es() returns a power4test_by_es object with all the elements (tests for different values of pop_es_values) combined.

The function as.power4test_by_es() returns a power4test_by_es object converted from the input object.

The print-method of power4test_by_es objects returns the object invisibly. It is called for its side-effect.

Details

The function power4test_by_es() regenerates datasets for a set of effect sizes (population values of a model parmeter) and does the stored tests in each of them.

Optionally, it can also be run on a object with no stored tests. In this case, additional arguments must be set to instruct power4test() on the tests to be conducted.

It is usually used to examine the power over a sets of effect sizes (population values).

The c method of power4test_by_es objects is used to combine tests from different runs of power4test_by_es().

The function as.power4test_by_es() is used to convert a power4test object to a power4test_by_es object, if it is not already one. Useful when concatenating power4test objects with power4test_by_es objects.

Examples


# Specify the model

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

# Specify the population values

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

sim_only <- power4test(nrep = 2,
                       model = model_simple_med,
                       pop_es = model_simple_med_es,
                       n = 100,
                       R = 40,
                       ci_type = "boot",
                       fit_model_args = list(fit_function = "lm"),
                       do_the_test = FALSE,
                       iseed = 1234)
#> Simulate the data:
#> Fit the model(s):
#> Generate bootstrap estimates:

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_indirect: x->m->y 

out <- power4test_by_es(test_out,
                            pop_es_name = "y ~ m",
                            pop_es_values = c(.10, .20))
#> 
#> Updating the simulation for new value: y ~ m = 0.100 
#> Re-simulate the data:
#> Fit the model(s):
#> Generate bootstrap estimates:
#> Update the test(s):
#> Update test_indirect: x->m->y :
#> 
#> Updating the simulation for new value: y ~ m = 0.200 
#> Re-simulate the data:
#> Fit the model(s):
#> Generate bootstrap estimates:
#> Update the test(s):
#> Update test_indirect: x->m->y :
out_reject <- rejection_rates(out)
out_reject
#> [test]: test_indirect: x->m->y 
#> [test_label]: Test 
#>     par    es   est   p.v reject r.cilo r.cihi
#> 1 y ~ m 0.100 0.043 1.000  0.000  0.000  0.000
#> 2 y ~ m 0.200 0.038 1.000  0.500 -0.193  1.193
#> Notes:
#> - par: The parameter being varied.
#> - es: The population value of 'par' in a trial.
#> - p.v: The proportion of valid replications.
#> - est: The mean of the estimates in a test across replications.
#> - reject: The proportion of 'significant' replications, that is, the
#>   rejection rate. If the null hypothesis is true, this is the Type I
#>   error rate. If the null hypothesis is false, this is the power.
#> - r.cilo,r.cihi: The confidence interval of the rejection rate, based
#>   on normal approximation.
#> - Refer to the tests for the meanings of other columns.

Usage

Arguments

Value

Details

See also

Examples