Title:	Power Analysis for Moderation and Mediation
Version:	0.1.1
Description:	Power analysis and sample size determination for moderation, mediation, and moderated mediation in models fitted by structural equation modelling using the 'lavaan' package by Rosseel (2012) <doi:10.18637/jss.v048.i02> or by multiple regression. The package 'manymome' by Cheung and Cheung (2024) <doi:10.3758/s13428-023-02224-z> is used to specify the indirect paths or conditional indirect paths to be tested.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.3
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
Config/testthat/parallel:	true
Depends:	R (≥ 4.4.0)
URL:	https://sfcheung.github.io/power4mome/
BugReports:	https://github.com/sfcheung/power4mome/issues
Imports:	lavaan, stats, manymome (≥ 0.2.8), pbapply, parallel, pgnorm, lmhelprs (≥ 0.4.2), psych, yaml, graphics, methods
Config/Needs/website:	rmarkdown
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-09-21 08:37:24 UTC; shufa
Author:	Shu Fai Cheung [aut, cre], Sing-Hang Cheung [aut], Wendie Yang [aut]
Maintainer:	Shu Fai Cheung <shufai.cheung@gmail.com>
Repository:	CRAN
Date/Publication:	2025-09-21 09:20:02 UTC

power4mome: Power Analysis for Moderation and Mediation

Description

Power analysis and sample size determination for moderation, mediation, and moderated mediation in models fitted by structural equation modelling using the 'lavaan' package by Rosseel (2012) doi:10.18637/jss.v048.i02 or by multiple regression. The package 'manymome' by Cheung and Cheung (2024) doi:10.3758/s13428-023-02224-z is used to specify the indirect paths or conditional indirect paths to be tested.

Author(s)

Maintainer: Shu Fai Cheung shufai.cheung@gmail.com (ORCID)

Authors:

• Sing-Hang Cheung (ORCID)

• Wendie Yang (ORCID)

See Also

Useful links:

• https://sfcheung.github.io/power4mome/

• Report bugs at https://github.com/sfcheung/power4mome/issues

Do a Test on Each Replication

Description

Do a test on each replication in the output of sim_out().

Usage

do_test(
  sim_all,
  test_fun,
  test_args = list(),
  map_names = c(fit = "fit"),
  results_fun = NULL,
  results_args = list(),
  parallel = FALSE,
  progress = FALSE,
  ncores = max(1, parallel::detectCores(logical = FALSE) - 1)
)

Arguments

Details

The function do_test() does an arbitrary test in each replication using the function set to test_fun.

Value

An object of the class test_out, which is a list of length equal to sim_all, one element for each replication. Each element of the list has two elements:

• test: The output of the function set to test_fun.

• test_results: The output of the function set to results_fun.

The role of do_test()

The function do_test() is used by the all-in-one function power4test(). Users usually do not call this function directly, though developers can use this function to develop other functions for power analysis, or to build their own workflows to do the power analysis.

Major Test-Related Arguments

The test function (test_fun)

The function set to test_fun, the test function, usually should work on the output of lavaan::sem(), lmhelprs::many_lm(), or stats::lm(), but can also be a function that works on the output of the function set to fit_function when calling fit_model() or power4test() (see fit_model_args).

The function has two default requirements.

First, it has an argument fit, to be set to the output of lavaan::sem() or another output stored in the element extra$fit of a replication in the sim_all object. (This requirement can be relaxed; see the section on map_names.)

That is, the function definition should be of this from: FUN(fit, ...). This is the form of all ⁠test_*⁠ functions in power4mome.

If other arguments are to be passed to the test function, they can be set to test_args as a named list.

Second, the test function must returns an output that (a) can be processed by the results function (see below), or (b) is of the required format for the output of a results function (see the next section). If it already returns an output of the required format, then there is no need to set the results function.

The results function (results_fun)

The test results will be extracted from the output of test_fun by the function set to results_fun, the results function. If the test_fun already returns an output of the expected format (see below), then set results_fun to NULL, the default. The output of test_fun will be used for estimating power.

The function set to results_fun must accept the output of test_fun, as the first argument, and return a named list (which can be a data frame) or a named vector with some of the following elements:

• est: Optional. The estimate of a parameter, if applicable.

• se: Optional. The standard error of the estimate, if applicable.

• cilo: Optional. The lower limit of the confidence interval, if applicable.

• cihi: Optional. The upper limit of the confidence interval, if applicable.

• sig: Required. If 1, the test is significant. If 0, the test is not significant. If the test cannot be done for any reason, it should be NA.

The results can then be used to estimate the power or Type I error of the test.

For example, if the null hypothesis is false, then the proportion of significant, that is, the mean of the values of sig across replications, is the power.

Built-in test functions

The package power4mome has some ready-to-use test functions:

• test_indirect_effect()

• test_cond_indirect()

• test_cond_indirect_effects()

• test_moderation()

• test_index_of_mome()

• test_parameters()

Please refer to their help pages for examples.

The argument map_names

This argument is for developers using a test function that has a different name for the argument of the fit object ("fit", the default).

If test_fun is set to a function that works on an output of, say, lavaan::sem() but the argument name for the output is not fit, the mapping can be changed by map_names.

For example, lavaan::parameterEstimates() receives an output of lavaan::sem() and reports the test results of model parameters. However, the argument name for the lavaan output is object. To instruct do_test() to do the test correctly when setting test_fun to lavaan::parameterEstimates, add map_names = c(object = "fit"). The element fit in a replication will then set to the argument object of lavaan::parameterEstimates().

The background for having the results_fun argument

In the early development of power4mome, test_fun is designed to accept existing functions from other packages, such as manymome::indirect_effect(). Their outputs are not of the required format for power analysis, and so results functions are needed to process their outputs. In the current version of power4mome, some ready-to-use test functions, usually wrappers of these existing functions from other packages, have been developed, and they no longer need results functions to process the output. The argument results_fun is kept for backward compatibility and advanced users to use test functions from other packages.

See Also

See power4test() for the all-in-one function that uses this function. See test_indirect_effect(), test_cond_indirect(), test_cond_indirect_effects(), test_moderation(), test_index_of_mome(), and test_parameters() for examples of test functions.

Examples

# Specify the population model

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

# Specify the effect sizes (population parameter values)

es <-
"
y ~ m: m
m ~ x: m
y ~ x: n
"

# Generate several simulated datasets

data_all <- sim_data(nrep = 5,
                     model = mod,
                     pop_es = es,
                     n = 100,
                     iseed = 1234)

# Fit the population model to each datasets

fit_all <- fit_model(data_all)

# Generate Monte Carlo estimates for forming Monte Carlo confidence intervals

mc_all <- gen_mc(fit_all,
                 R = 50,
                 iseed = 4567)

# Combine the results to a 'sim_all' object
sim_all <- sim_out(data_all = data_all,
                   fit = fit_all,
                   mc_out = mc_all)

# Test the indirect effect in each replication
# Set `parallel` to TRUE for faster, usually much faster, analysis
# Set `progress` to TRUE to display the progress of the analysis

test_all <- do_test(sim_all,
                    test_fun = test_indirect_effect,
                    test_args = list(x = "x",
                                     m = "m",
                                     y = "y",
                                     mc_ci = TRUE),
                    parallel = FALSE,
                    progress = FALSE)

# The result for each dataset
lapply(test_all, function(x) x$test_results)

Fit a Model to a List of Datasets

Description

Get the output of sim_data() and fit a model to each of the stored datasets.

Usage

fit_model(
  data_all = NULL,
  model = NULL,
  fit_function = "lavaan",
  arg_data_name = "data",
  arg_model_name = "model",
  arg_group_name = "group",
  ...,
  fit_out = NULL,
  parallel = FALSE,
  progress = FALSE,
  ncores = max(1, parallel::detectCores(logical = FALSE) - 1)
)

Arguments

Details

By default, the function fit_model()

• extracts the model stored in the output of sim_data(),

• fits the model to each dataset simulated using fit_function, default to "lavaan" and lavaan::sem() will be called,

• and returns the results.

If the datasets were generated from a multigroup model when calling sim_data(), a multigroup model is fitted.

Value

An object of the class fit_out, which is a list of the output of fit_function (lavaan::sem() by default). If an error occurred when fitting the model to a dataset, then this element will be the error message from the fit function.

The role of fit_model()

This function is used by the all-in-one function power4test(). Users usually do not call this function directly, though developers can use this function to customize the model fitting step in power analysis.

See Also

See power4test() for the all-in-one function that uses this function, and sim_data() for the function generating datasets for this function.

Examples

# Specify the population model

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

# Specify the effect sizes (population parameter values)

es <-
"
y ~ m: m
m ~ x: m
y ~ x: n
"

# Generate several simulated datasets

data_all <- sim_data(nrep = 5,
                     model = mod,
                     pop_es = es,
                     n = 100,
                     iseed = 1234)

# Fit the population model to each datasets

fit_all <- fit_model(data_all)
fit_all[[1]]

# Fit the population model using the MLR estimator

fit_all_mlr <- fit_model(data_all,
                         estimator = "MLR")
fit_all_mlr[[1]]

# Fit a model different from the population model,
# with the MLR estimator

mod2 <-
"m ~ x
 y ~ m"

fit_all_mlr2 <- fit_model(data_all,
                          mod2,
                          estimator = "MLR")
fit_all_mlr2[[1]]

Generate Bootstrap Estimates

Description

Get a list of the output of lavaan::sem() or lmhelprs::many_lm() and generate bootstrap estimates of model parameters.

Usage

gen_boot(
  fit_all,
  R = 100,
  ...,
  iseed = NULL,
  parallel = FALSE,
  progress = FALSE,
  ncores = max(1, parallel::detectCores(logical = FALSE) - 1),
  compute_implied_stats = FALSE
)

Arguments

Details

The function gen_boot() simply calls manymome::do_boot() on each output of lavaan::sem() or lmhelprs::many_lm() in fit_all. The simulated estimates can then be used to test effects such as indirect effects, usually by functions from the manymome package, such as manymome::indirect_effect().

Value

A boot_list object, which is a list of the output of manymome::do_boot().

The role of gen_boot()

This function is used by the all-in-one function power4test(). Users usually do not call this function directly, though developers can use this function to customize the workflow of the power analysis.

See Also

See power4test() for the all-in-one function that uses this function.

Examples

# Specify the population model

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

# Specify the effect sizes (population parameter values)

es <-
"
y ~ m: m
m ~ x: m
y ~ x: n
"

# Generate several simulated datasets

data_all <- sim_data(nrep = 2,
                     model = mod,
                     pop_es = es,
                     n = 50,
                     iseed = 1234)

# Fit the population model to each datasets

fit_all <- fit_model(data_all)

# Generate bootstrap estimates for each replication

boot_all <- gen_boot(fit_all,
                     R = 10,
                     iseed = 4567)
boot_all

Generate Monte Carlo Estimates

Description

Get a list of the output of lavaan::sem() and generate Monte Carlo estimates of model parameters.

Usage

gen_mc(
  fit_all,
  R = 100,
  ...,
  iseed = NULL,
  parallel = FALSE,
  progress = FALSE,
  ncores = max(1, parallel::detectCores(logical = FALSE) - 1),
  compute_implied_stats = FALSE
)

Arguments

Details

The function gen_mc() simply calls manymome::do_mc() on each output of lavaan::sem() in fit_all. The simulated estimates can then be used to test effects such as indirect effects, usually by functions from the manymome package, such as manymome::indirect_effect().

Value

An mc_list object, which is a list of the output of manymome::do_mc().

The role of gen_mc()

This function is used by the all-in-one function power4test(). Users usually do not call this function directly, though developers can use this function to customize the workflow of the power analysis.

See Also

See power4test() for the all-in-one function that uses this function.

Examples

# Specify the population model

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

# Specify the effect sizes (population parameter values)

es <-
"
y ~ m: m
m ~ x: m
y ~ x: n
"

# Generate several simulated datasets

data_all <- sim_data(nrep = 5,
                     model = mod,
                     pop_es = es,
                     n = 100,
                     iseed = 1234)

# Fit the population model to each datasets

fit_all <- fit_model(data_all)

# Generate Monte Carlo estimates for each replication

mc_all <- gen_mc(fit_all,
                 R = 100,
                 iseed = 4567)

mc_all

Plot a Power Curve

Description

Plotting the results in a 'power_curve' object, such as the estimated power against sample size, or the results of power4test_by_n() or power4test_by_es().

Usage

## S3 method for class 'power_curve'
plot(
  x,
  what = c("ci", "power_curve"),
  main = paste0("Power Curve ", "(Predictor: ", switch(x$predictor, n = "Sample Size", es
    = "Effect Size"), ")"),
  xlab = switch(x$predictor, n = "Sample Size", es = "Effect Size"),
  ylab = "Estimated Power",
  pars_ci = list(),
  type = "l",
  ylim = c(0, 1),
  ci_level = 0.95,
  ...
)

## S3 method for class 'power4test_by_n'
plot(
  x,
  what = c("ci", "power_curve"),
  main = "Estimated Power vs. Sample Size",
  xlab = "Sample Size",
  ylab = "Estimated Power",
  pars_ci = list(),
  type = "l",
  ylim = c(0, 1),
  ci_level = 0.95,
  ...
)

## S3 method for class 'power4test_by_es'
plot(
  x,
  what = c("ci", "power_curve"),
  main = paste0("Estimated Power vs. Effect Size / Parameter (", attr(x[[1]],
    "pop_es_name"), ")"),
  xlab = paste0("Effect Size / Parameter (", attr(x[[1]], "pop_es_name"), ")"),
  ylab = "Estiamted Power",
  pars_ci = list(),
  type = "l",
  ylim = c(0, 1),
  ci_level = 0.95,
  ...
)

Arguments

Details

The plot method of power_curve objects currently plots the relation between estimated power and the predictor. Other elements can be requested (see the argument what), and they can be customized individually.

Value

The plot-methods return x invisibly. They are called for their side effects.

See Also

power_curve(), power4test_by_n(), and power4test_by_es().

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: s
"

# Simulate datasets to check the model

sim_only <- power4test(nrep = 10,
                       model = model_simple_med,
                       pop_es = model_simple_med_es,
                       n = 50,
                       fit_model_args = list(fit_function = "lm"),
                       do_the_test = FALSE,
                       iseed = 1234,
                       parallel = FALSE,
                       progress = FALSE)

# By n: Do a test for different sample sizes
# Set `parallel` to TRUE for faster, usually much faster, analysis
# Set `progress` to TRUE to display the progress of the analysis

out1 <- power4test_by_n(sim_only,
                        nrep = 10,
                        test_fun = test_parameters,
                        test_args = list(par = "y~x"),
                        n = c(25, 50, 100),
                        by_seed = 1234,
                        parallel = FALSE,
                        progress = FALSE)

pout1 <- power_curve(out1)
pout1
plot(pout1)

# By pop_es: Do a test for different population values of a model parameter
# Set `parallel` to TRUE for faster, usually much faster, analysis
# Set `progress` to TRUE to display the progress of the analysis

out2 <- power4test_by_es(sim_only,
                             nrep = 10,
                             test_fun = test_parameters,
                             test_args = list(par = "y~x"),
                             pop_es_name = "y ~ x",
                             pop_es_values = c(0, .3, .5),
                             by_seed = 1234,
                             parallel = FALSE,
                             progress = FALSE)

pout2 <- power_curve(out2)
plot(pout2)

Plot The Results of 'x_from_power'

Description

It plots the results of 'x_from_power', such as the estimated power against sample size.

Usage

## S3 method for class 'x_from_power'
plot(
  x,
  what = c("ci", "power_curve", "final_x", "final_power", "target_power", switch(x$x, n =
    "sig_area", es = NULL)),
  text_what = c("final_x", "final_power", switch(x$x, n = "sig_area", es = NULL)),
  digits = 3,
  main = paste0("Power Curve ", "(Target Power: ", formatC(x$target_power, digits =
    digits, format = "f"), ")"),
  xlab = NULL,
  ylab = "Estimated Power",
  ci_level = 0.95,
  pars_ci = list(),
  pars_power_curve = list(),
  pars_ci_final_x = list(lwd = 2, length = 0.2, col = "blue"),
  pars_target_power = list(lty = "dashed", lwd = 2, col = "black"),
  pars_final_x = list(lty = "dotted"),
  pars_final_power = list(lty = "dotted", col = "blue"),
  pars_text_final_x = list(y = 0, pos = 3, cex = 1),
  pars_text_final_power = list(pos = 3, cex = 1),
  pars_sig_area = list(col = adjustcolor("lightblue", alpha.f = 0.1)),
  pars_text_sig_area = list(cex = 1),
  ...
)

## S3 method for class 'n_region_from_power'
plot(
  x,
  what = c("ci", "power_curve", "final_x", "final_power", "target_power", "sig_area"),
  text_what = c("final_x", "final_power", "sig_area"),
  digits = 3,
  main = paste0("Power Curve ", "(Target Power: ", formatC(x$below$target_power, digits =
    digits, format = "f"), ")"),
  xlab = NULL,
  ylab = "Estimated Power",
  ci_level = 0.95,
  pars_ci = list(),
  pars_power_curve = list(),
  pars_ci_final_x = list(lwd = 2, length = 0.2, col = "blue"),
  pars_target_power = list(lty = "dashed", lwd = 2, col = "black"),
  pars_final_x = list(lty = "dotted"),
  pars_final_power = list(lty = "dotted", col = "blue"),
  pars_text_final_x = list(y = 0, pos = 3, cex = 1),
  pars_text_final_power = list(cex = 1),
  pars_sig_area = list(col = adjustcolor("lightblue", alpha.f = 0.1)),
  pars_text_sig_area = list(cex = 1),
  ...
)

Arguments

Details

The plot method of x_from_power objects currently plots the relation between estimated power and the values examined by x_from_power(). Other elements can be requested (see the argument what), and they can be customized individually.

The plot-method for n_region_from_power objects is a modified version of the plot-method for x_from_power. It plots the results of two runs of n_from_power() in one plot. It is otherwise similar to the plot-method for x_from_power.

Value

The plot-method of x_from_power returns x invisibly. It is called for its side effect.

The plot-method of n_region_from_power returns x invisibly. It is called for its side effect.

See Also

x_from_power()

Examples

# Specify the population model

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

# Specify the population values

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

# Generate the datasets

sim_only <- power4test(nrep = 10,
                       model = mod,
                       pop_es = mod_es,
                       n = 100,
                       do_the_test = FALSE,
                       iseed = 1234)

# Do a test

test_out <- power4test(object = sim_only,
                       test_fun = test_parameters,
                       test_args = list(pars = "m~x"))

# Determine the sample size with a power of .80 (default)

power_vs_n <- x_from_power(test_out,
                           x = "n",
                           progress = TRUE,
                           target_power = .80,
                           final_nrep = 10,
                           max_trials = 1,
                           seed = 2345)
plot(power_vs_n)

Parse YAML-Stye Values For 'pop_es'

Description

Convert a YAML string to a vector or list of pop_es specification.

Usage

pop_es_yaml(text)

Arguments

Details

The function pop_es_yaml() allows users to specify population values of a model using one single string, as in 'lavaan' model syntax.

Value

Either a named vector (for a single-group model) or a list of named vector (for a multigroup model) of the population values of the parameters (the effect sizes).

Specify 'pop_es' Using a Multiline String

When setting the argument pop_es, instead of using a named vector or named list for pop_es, the population values of model parameters can also be specified using a multiline string, as illustrated below, to be parsed by pop_es_yaml().

Single-Group Model

This is an example of the multiline string for a single-group model:

y ~ m: l
m ~ x: m
y ~ x: nil

The string must follow this format:

• Each line starts with ⁠tag:⁠.

  • tag can be the name of a parameter, in lavaan model syntax format.

    • For example, m ~ x denotes the path from x to m.

  • A tag in lavaan model syntax can specify more than one parameter using +.

    • For example, y ~ m + x denotes the two paths from m and x to y.

  • Alternatively, the tag can be either .beta. or .cov..

    • Use .beta. to set the default values for all regression coefficients.

    • Use .cov. to set the default values for all correlations of exogenous variables (e.g., predictors).

• After each tag is the value of the population value:

  -nil for nil (zero),

  • s for small,

  • m for medium, and

  • l for large.

  • si, mi, and li for small, medium, and large a standardized indirect effect, respectively.

  Note: n cannot be used in this mode.

  The value for each label is determined by es1 and es2 as described in ptable_pop().

  • The value can also be set to a numeric value, such as .30 or -.30.

This is another example:

.beta.: s
y ~ m: l

In this example, all regression coefficients are small, while the path from m to y is large.

Multigroup Model

This is an example of the string for a multigroup model:

y ~ m: l
m ~ x:
  - nil
  - s
y ~ x: nil

The format is similar to that for a single-group model. If a parameter has the same value for all groups, then the line can be specified as in the case of a single-group model: tag: value.

If a parameter has different values across groups, then it must be in this format:

• A line starts with the tag, followed by two or more lines. Each line starts with a hyphen - and the value for a group.

For example:

m ~ x:
  - nil
  - s

This denotes that the model has two groups. The values of the path from x to m for the two groups are 0 (nil) and small (s), respectively.

Another equivalent way to specify the values are using ⁠[]⁠, on the same line of a tag.

For example:

m ~ x: [nil, s]

The number of groups is inferred from the number of values for a parameter. Therefore, if a tag has more than one value, each tag must has the same number of value, or only one value.

The tag .beta. and .cov. can also be used for multigroup models.

Which Approach To Use

Note that using named vectors or named lists is more reliable. However, using a multiline string is more user-friendly. If this method failed, please use named vectors or named list instead.

Technical Details

The multiline string is parsed by yaml::read_yaml(). Therefore, the format requirement is actually that of YAML. Users knowledgeable of YAML can use other equivalent way to specify the string.

See Also

ptable_pop(), power4test(), and other functions that have the pop_es argument.

Examples

mod_es <- c("y ~ m" = "l",
            "m ~ x" = "m",
            "y ~ x" = "nil")

mod_es_yaml <-
"
y ~ m: l
m ~ x: m
y ~ x: nil
"

pop_es_yaml(mod_es_yaml)

Estimate the Power of a Test

Description

An all-in-one function that receives a model specification, generates datasets, fits a model, does the target test, and returns the test results.

Usage

power4test(
  object = NULL,
  nrep = NULL,
  ptable = NULL,
  model = NULL,
  pop_es = NULL,
  standardized = TRUE,
  n = NULL,
  number_of_indicators = NULL,
  reliability = NULL,
  x_fun = list(),
  e_fun = list(),
  process_data = NULL,
  fit_model_args = list(),
  R = NULL,
  ci_type = "mc",
  gen_mc_args = list(),
  gen_boot_args = list(),
  test_fun = NULL,
  test_args = list(),
  map_names = c(fit = "fit"),
  results_fun = NULL,
  results_args = list(),
  test_name = NULL,
  test_note = NULL,
  do_the_test = TRUE,
  sim_all = NULL,
  iseed = NULL,
  parallel = FALSE,
  progress = TRUE,
  ncores = max(1, parallel::detectCores(logical = FALSE) - 1),
  es1 = c(n = 0, nil = 0, s = 0.1, m = 0.3, l = 0.5, si = 0.141, mi = 0.361, li = 0.51),
  es2 = c(n = 0, nil = 0, s = 0.05, m = 0.1, l = 0.15),
  es_ind = c("si", "mi", "li"),
  n_std = 1e+05,
  std_force_monte_carlo = FALSE
)

## S3 method for class 'power4test'
print(
  x,
  what = c("data", "test"),
  digits = 3,
  digits_descriptive = 2,
  data_long = FALSE,
  test_long = FALSE,
  fit_to_all_args = list(),
  ...
)

Arguments

Details

The function power4test() is an all-in-one function for estimating the power of a test for a model, given the sample size and effect sizes (population values of model parameters).

Value

An object of the class power4test, which is a list with two elements:

• sim_all: The output of sim_out().

• test_all: A named list of the output of do_test(). The names are the values of test_name. This list can have more than one test because a call to power4test() can add new tests to a power4test object.

The print method of power4test returns x invisibly. Called for its side effect.

Workflow

This is the workflow:

• If object is an output of the output of a previous call to power4test() with do_the_test set to FALSE and so has only the model and the simulated data, the following steps will be skipped and go directly to doing the test.

  • Call sim_data() to determine the population model and generate the datasets, using arguments such as model and pop_es.

  • Call fit_model() to fit a model to each of the datasets, which is the population model by default.

  • If R is not NULL and ci_type = "mc", call gen_mc() to generate Monte Carlo estimates using manymome::do_mc(). The estimates can be used by supported functions such as test_indirect_effect().

  • If R is not NULL and ci_type = "boot", call gen_boot() to generate bootstrap estimates using manymome::do_boot(). The estimates can be used by supported functions such as test_indirect_effect().

  • Merge the results into a sim_out object by calling sim_out().

  • If do_the_test is FALSE, skip the remaining steps and return a power4test object, which contains only the data generated and optionally the Monte Carlo or bootstrap estimates.

• If do_the_test is TRUE, do the test.

  • do_test() will be called to do the test in the fit output of each dataset.

• Return a power4test object which include the output of sim_out and, if do_the_test is TRUE, the output of do_test().

This function is to be used when users are interested only in the power of one or several tests on a particular aspect of the model, such as a parameter, given a specific effect sizes and sample sizes.

Detailed description on major arguments can be found in sections below.

NOTE: The technical internal workflow of of power4test() can be found in this page: https://sfcheung.github.io/power4mome/articles/power4test_workflow.html.

Updating a Condition

The function power4test() can also be used to update a condition when only some selected aspects is to be changed.

For example, instead of calling this function with all the arguments set just to change the sample size, it can be called by supplying an existing power4test object and set only n to a new sample size. The data and the tests will be updated automatically. See the examples for an illustration.

Adding Another Test

The function power4test() can also be used to add a test to the output from a previous call to power4test().

For example, after simulating the datasets and doing one test, the output can be set to object of power4test(), and set only test_fun and, optionally, test_fun_args to do one more test on the generated datasets. The output will be the original object with the results of the new test added. See the examples for an illustration.

Model Fitting Arguments ('fit_model_args')

For power analysis, usually, the population model (model) is to be fitted, and there is no need to set fit_model_args.

If power analysis is to be conducted for fitting a model that is not the population model, of if non-default settings are desired when fitting a model, then the argument fit_model_args needed to be set to customize the call to fit_model().

For example, users may want to examine the power of a test when a misspecified model is fitted, or the power of a test when MLR is used as the estimator when calling lavaan::sem().

See the help page of fit_model() for some examples.

Specify the Population Model by 'model'

Single-Group Model

For a single-group model, model should be a lavaan model syntax string of the form of the model. The population values of the model parameters are to be determined by pop_es.

If the model has latent factors, the syntax in model should specify only the structural model for the latent factors. There is no need to specify the measurement part. Other functions will generate the measurement part on top of this model.

For example, this is a simple mediation model:

"m ~ x
 y ~ m + x"

Whether m, x, and y denote observed variables or latent factors are determined by other functions, such as power4test().

Multigroup Model

Because the model is the population model, equality constraints are irrelevant and the model syntax specifies only the form of the model. Therefore, model is specified as in the case of single group models.

Specify 'pop_es' Using Named Vectors

The argument pop_es is for specifying the population values of model parameters. This section describes how to do this using named vectors.

Single-Group Model

If pop_es is specified by a named vector, it must follow the convention below.

• The names of the vectors are lavaan names for the selected parameters. For example, m ~ x denotes the path from x to m.

• Alternatively, the names can be either ".beta." or ".cov.". Use ".beta." to set the default values for all regression coefficients. Use ".cov." to set the default values for all correlations of exogenous variables (e.g., predictors).

• The names can also be of this form: ".ind.(<path>)", whether ⁠<path>⁠ denote path in the model. For example, ".ind.(x->m->y)" denotes the path from x through m to y. Alternatively, the lavaan symbol ~ can also be used: ".ind.(y~m~x)". This form is used to set the indirect effect (standardized, by default) along this path. The value for this name will override other settings.

• If using lavaan names, can specify more than one parameter using +. For example, y ~ m + x denotes the two paths from m and x to y.

• The value of each element can be the label for the effect size: n for nil, s for small, m for medium, and l for large. The value for each label is determined by es1 and es2. See the section on specifying these two arguments.

• The value of pop_es can also be set to a value, but it must be quoted as a string, such as "y ~ x" = ".31".

This is an example:

c(".beta." = "s",
  "m1 ~ x" = "-m",
  "m2 ~ m1" = "l",
  "y ~ x:w" = "s")

In this example,

• All regression coefficients are set to small (s) by default, unless specified otherwise.

• The path from x to m1 is set to medium and negative (-m).

• The path from m1 to m2 is set to large (l).

• The coefficient of the product term x:w when predicting y is set to small (s).

Indirect Effect

When setting an indirect effect to a symbol (default: "si", "mi", "li", with "i" added to differentiate them from the labels for a direct path), the corresponding value is used to determine the population values of all component paths along the pathway. All the values are assumed to be equal. Therefore, ".ind.(x->m->y)" = ".20" is equivalent to setting m ~ x and y ~ m to the square root of .20, such that the corresponding indirect effect is equal to the designated value.

This behavior, though restricted, is for quick manipulation of the indirect effect. If different values along a pathway, set the value for each path directly.

Only nonnegative value is supported. Therefore, ".ind.(x->m->y)" = "-si" and ".ind.(x->m->y)" = "-.20" will throw an error.

Multigroup Model

The argument pop_es also supports multigroup models.

For pop_es, instead of named vectors, named list of named vectors should be used.

• The names are the parameters, or keywords such as .beta. and .cov., like specifying the population values for a single group model.

• The elements are character vectors. If it has only one element (e.g., a single string), then it is the the population value for all groups. If it has more than one element (e.g., a vector of three strings), then they are the population values of the groups. For a model of k groups, each vector must have either k elements or one element.

This is an example:

list("m ~ x" = "m",
     "y ~ m" = c("s", "m", "l"))

In this model, the population value of the path m ~ x is medium (m) for all groups, while the population values for the path y ~ m are small (s), medium (m), and large (l), respectively.

Specify 'pop_es' Using a Multiline String

When setting the argument pop_es, instead of using a named vector or named list for pop_es, the population values of model parameters can also be specified using a multiline string, as illustrated below, to be parsed by pop_es_yaml().

Single-Group Model

This is an example of the multiline string for a single-group model:

y ~ m: l
m ~ x: m
y ~ x: nil

The string must follow this format:

• Each line starts with ⁠tag:⁠.

  • tag can be the name of a parameter, in lavaan model syntax format.

    • For example, m ~ x denotes the path from x to m.

  • A tag in lavaan model syntax can specify more than one parameter using +.

    • For example, y ~ m + x denotes the two paths from m and x to y.

  • Alternatively, the tag can be either .beta. or .cov..

    • Use .beta. to set the default values for all regression coefficients.

    • Use .cov. to set the default values for all correlations of exogenous variables (e.g., predictors).

• After each tag is the value of the population value:

  -nil for nil (zero),

  • s for small,

  • m for medium, and

  • l for large.

  • si, mi, and li for small, medium, and large a standardized indirect effect, respectively.

  Note: n cannot be used in this mode.

  The value for each label is determined by es1 and es2 as described in ptable_pop().

  • The value can also be set to a numeric value, such as .30 or -.30.

This is another example:

.beta.: s
y ~ m: l

In this example, all regression coefficients are small, while the path from m to y is large.

Multigroup Model

This is an example of the string for a multigroup model:

y ~ m: l
m ~ x:
  - nil
  - s
y ~ x: nil

The format is similar to that for a single-group model. If a parameter has the same value for all groups, then the line can be specified as in the case of a single-group model: tag: value.

If a parameter has different values across groups, then it must be in this format:

• A line starts with the tag, followed by two or more lines. Each line starts with a hyphen - and the value for a group.

For example:

m ~ x:
  - nil
  - s

This denotes that the model has two groups. The values of the path from x to m for the two groups are 0 (nil) and small (s), respectively.

Another equivalent way to specify the values are using ⁠[]⁠, on the same line of a tag.

For example:

m ~ x: [nil, s]

The number of groups is inferred from the number of values for a parameter. Therefore, if a tag has more than one value, each tag must has the same number of value, or only one value.

The tag .beta. and .cov. can also be used for multigroup models.

Which Approach To Use

Note that using named vectors or named lists is more reliable. However, using a multiline string is more user-friendly. If this method failed, please use named vectors or named list instead.

Technical Details

The multiline string is parsed by yaml::read_yaml(). Therefore, the format requirement is actually that of YAML. Users knowledgeable of YAML can use other equivalent way to specify the string.

Set the Values for Effect Size Labels ('es1' and 'es2')

The vector es1 is for correlations, regression coefficients, and indirect effect, and the vector es2 is for for standardized moderation effect, the coefficients of a product term. These labels are to be used in interpreting the specification in pop_es.

Set 'number_of_indicators' and 'reliability'

The arguments number_of_indicators and reliability are used to specify the number of indicators (e.g., items) for each factor, and the population reliability coefficient of each factor, if the variables in the model syntax are latent variables.

Single-Group Model

If a variable in the model is to be replaced by indicators in the generated data, set number_of_indicators to a named numeric vector. The names are the variables of variables with indicators, as appeared in the model syntax. The value of each name is the number of indicators.

The argument reliability should then be set a named numeric vector (or list, see the section on multigroup models) to specify the population reliability coefficient ("omega") of each set of indicators. The population standardized factor loadings are then computed to ensure that the population reliability coefficient is of the target value.

These are examples for a single group model:

number of indicator = c(m = 3, x = 4, y = 5)

The numbers of indicators for m, x, and y are 3, 4, and 5, respectively.

reliability = c(m = .90, x = .80, y = .70)

The population reliability coefficients of m, x, and y are .90, .80, and .70, respectively.

Multigroup Models

Multigroup models are supported. The number of groups is inferred from pop_es (see the help page of ptable_pop()), or directly from ptable.

For a multigroup model, the number of indicators for each variable must be the same across groups.

However, the population reliability coefficients can be different across groups. For a multigroup model of k groups, with one or more population reliability coefficients differ across groups, the argument reliability should be set to a named list. The names are the variables to which the population reliability coefficients are to be set. The element for each name is either a single value for the common reliability coefficient, or a numeric vector of the reliability coefficient of each group.

This is an example of reliability for a model with 2 groups:

reliability = list(x = .80, m = c(.70, .80))

The reliability coefficients of x are .80 in all groups, while the reliability coefficients of m are .70 in one group and .80 in another.

Equal Numbers of Indicators and/or Reliability Coefficients

If all variables in the model has the same number of indicators, number_of_indicators can be set to one single value.

Similarly, if all sets of indicators have the same population reliability in all groups, reliability can also be set to one single value.

Specify The Distributions of Exogenous Variables Or Error Terms Using 'x_fun'

By default, variables and error terms are generated from a multivariate normal distribution. If desired, users can supply the function used to generate an exogenous variable and error term by setting x_fun to a named list.

The names of the list are the variables for which a user function will be used to generate the data.

Each element of the list must also be a list. The first element of this list, can be unnamed, is the function to be used. If other arguments need to be supplied, they should be included as named elements of this list.

For example:

x_fun = list(x = list(power4mome::rexp_rs),
             w = list(power4mome::rbinary_rs,
                      p1 = .70)))

The variables x and w will be generated by user-supplied functions.

For x, the function is power4mome::rexp_rs. No additional argument when calling this function.

For w, the function is power4mome::rbinary_rx. The argument p1 = .70 will be passed to this function when generating the values of w.

If a variable is an endogenous variable (e.g., being predicted by another variable in a model), then x_fun is used to generate its error term. Its implied population distribution may still be different from that generate by x_fun because the distribution also depends on the distribution of other variables predicting it.

These are requirements for the user-functions:

• They must return a numeric vector.

• They mush has an argument n for the number of values.

• The population mean and standard deviation of the generated values must be 0 and 1, respectively.

The package power4mome has helper functions for generating values from some common nonnormal distributions and then scaling them to have population mean and standard deviation equal to 0 and 1 (by default), respectively. These are some of them:

• rbinary_rs().

• rexp_rs().

• rbeta_rs().

• rlnorm_rs().

• rpgnorm_rs().

To use x_fun, the variables must have zero covariances with other variables in the population. It is possible to generate nonnormal multivariate data but we believe this is rarely needed when estimating power before having the data.

Major Test-Related Arguments

The test function (test_fun)

The function set to test_fun, the test function, usually should work on the output of lavaan::sem(), lmhelprs::many_lm(), or stats::lm(), but can also be a function that works on the output of the function set to fit_function when calling fit_model() or power4test() (see fit_model_args).

The function has two default requirements.

First, it has an argument fit, to be set to the output of lavaan::sem() or another output stored in the element extra$fit of a replication in the sim_all object. (This requirement can be relaxed; see the section on map_names.)

That is, the function definition should be of this from: FUN(fit, ...). This is the form of all ⁠test_*⁠ functions in power4mome.

If other arguments are to be passed to the test function, they can be set to test_args as a named list.

Second, the test function must returns an output that (a) can be processed by the results function (see below), or (b) is of the required format for the output of a results function (see the next section). If it already returns an output of the required format, then there is no need to set the results function.

The results function (results_fun)

The test results will be extracted from the output of test_fun by the function set to results_fun, the results function. If the test_fun already returns an output of the expected format (see below), then set results_fun to NULL, the default. The output of test_fun will be used for estimating power.

The function set to results_fun must accept the output of test_fun, as the first argument, and return a named list (which can be a data frame) or a named vector with some of the following elements:

• est: Optional. The estimate of a parameter, if applicable.

• se: Optional. The standard error of the estimate, if applicable.

• cilo: Optional. The lower limit of the confidence interval, if applicable.

• cihi: Optional. The upper limit of the confidence interval, if applicable.

• sig: Required. If 1, the test is significant. If 0, the test is not significant. If the test cannot be done for any reason, it should be NA.

The results can then be used to estimate the power or Type I error of the test.

For example, if the null hypothesis is false, then the proportion of significant, that is, the mean of the values of sig across replications, is the power.

Built-in test functions

The package power4mome has some ready-to-use test functions:

• test_indirect_effect()

• test_cond_indirect()

• test_cond_indirect_effects()

• test_moderation()

• test_index_of_mome()

• test_parameters()

Please refer to their help pages for examples.

The argument map_names

This argument is for developers using a test function that has a different name for the argument of the fit object ("fit", the default).

If test_fun is set to a function that works on an output of, say, lavaan::sem() but the argument name for the output is not fit, the mapping can be changed by map_names.

For example, lavaan::parameterEstimates() receives an output of lavaan::sem() and reports the test results of model parameters. However, the argument name for the lavaan output is object. To instruct do_test() to do the test correctly when setting test_fun to lavaan::parameterEstimates, add map_names = c(object = "fit"). The element fit in a replication will then set to the argument object of lavaan::parameterEstimates().

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
"

# Set nrep to a large number in real analysis, such as 400
# Set `parallel` to TRUE for faster, usually much faster, analysis
# Set `progress` to TRUE to display the progress of the analysis

out <- power4test(nrep = 10,
                  model = model_simple_med,
                  pop_es = model_simple_med_es,
                  n = 100,
                  test_fun = test_parameters,
                  test_args = list(pars = "m~x"),
                  iseed = 1234,
                  parallel = FALSE,
                  progress = TRUE)

print(out,
      test_long = TRUE)

# Change the sample size

out1 <- power4test(out,
                   n = 200,
                   iseed = 1234,
                   parallel = FALSE,
                   progress = TRUE)

print(out1,
      test_long = TRUE)

# Add one more test

out2 <- power4test(out,
                   test_fun = test_parameters,
                   test_args = list(pars = "y~x"),
                   parallel = FALSE,
                   progress = TRUE)

print(out2,
      test_long = TRUE)

Power By Effect Sizes

Description

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

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.

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.

See Also

power4test()

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)

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))

out <- power4test_by_es(test_out,
                            pop_es_name = "y ~ m",
                            pop_es_values = c(.10, .20))
out_reject <- rejection_rates(out)
out_reject

Power By Sample Sizes

Description

Estimate power for a set of sample sizes.

Usage

power4test_by_n(
  object,
  n = NULL,
  progress = TRUE,
  ...,
  by_seed = NULL,
  by_nrep = NULL,
  save_sim_all = TRUE
)

## S3 method for class 'power4test_by_n'
c(..., sort = TRUE, skip_checking_models = FALSE)

as.power4test_by_n(original_object)

## S3 method for class 'power4test_by_n'
print(x, print_all = FALSE, ...)

Arguments

Details

The function power4test_by_n() regenerates datasets for a set of sample sizes 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 set of sample sizes.

The c method of power4test_by_n objects is used to combine tests from different runs of power4test_by_n().

The function as.power4test_by_n() is used to convert a power4test object to a power4test_by_n object, if it is not already one. Useful when concatenating power4test objects with power4test_by_n objects.

Value

The function power4test_by_n() returns a power4test_by_n object, which is a list of power4test objects, one for each sample size.

The method c.power4test_by_n() returns a power4test_by_n object with all the elements (tests for different sample sizes) combined.

The function as.power4test_by_n() returns a power4test_by_n object converted from the input object.

The print-method of power4test_by_n objects returns the object invisibly. It is called for its side-effect.

See Also

power4test()

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)

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))

out <- power4test_by_n(test_out,
                       n = c(100, 110, 120))
out_reject <- rejection_rates(out)
out_reject

Power Curve

Description

Estimate the relation between power and a characteristic, such as sample size or population effect size (population value of a model parameter).

Usage

power_curve(
  object,
  formula = NULL,
  start = NULL,
  lower_bound = NULL,
  upper_bound = NULL,
  nls_args = list(),
  nls_control = list(),
  verbose = FALSE,
  models = c("nls", "logistic", "lm")
)

## S3 method for class 'power_curve'
print(x, data_used = FALSE, digits = 3, right = FALSE, row.names = FALSE, ...)

Arguments

Details

The function power_curve() retrieves the information from the output of power4test_by_n() or power4test_by_es(), and estimate the power curve: the relation between the characteristic varied, sample size for power4test_by_n() and the population effect size for power4test_by_es(), and the rejection rate of the test conducted by power4test_by_n() or power4test_by_es(). This rejection rate is the power when the null hypothesis is false (e.g., the population value of the effect size being tested is nonzero).

The model fitted is not intended to be a precise model for the relation across a wide range. It is only a crude estimate based on the limited number of values of the characteristic (e.g., sample size) examined, which can be as small as four or even smaller. The model is intended to be used for only for the range covered, and for estimating the probable sample size or effect size with a desirable level of power. This value should then be studied by higher precision through simulation using functions such as power4test().

These are the models to be tried, in the following order:

• One or nonlinear models, to be fitted by stats::nls(). If several models are specified, all will be fitted and the one with the smallest deviance will be used.

• If all the nonlinear models failed, for whatever reason, a logistic regression will be fitted by stats::glm() to predict the binary significant test results.

• If the logistic model also failed, for whatever reason, a simple linear regression model will be fitted. Although the power curve is nonlinear across a wide range of, say, sample size, a linear model can still be a good enough approximation for a narrow range of the predictor.

The output can then be plotted to visualize the power curve, using the plot method (plot.power_curve()) for the output of power_curve().

This function can be used directly, but is also used internally by functions such as x_from_power().

Value

It returns a list which is a power_curve object, with the following elements:

• fit: The model fitted, which is the output of stats::nls(), stats::glm(), or stats::lm().

• reject_df: The table of reject rates and other characteristics, which is generated by rejection_rates().

• predictor: The predictor or the power curve, ether "n" (sample size) or "es" (population effect size).

• call: The call used to run this function.

The print method of power_curve object returns x invisibly. Called for its side-effect.

See Also

power4test_by_n() and power4test_by_es() for the output supported by power_curve(), plot.power_curve() for the plot method and predict.power_curve() for the predict method of the output of power_curve().

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: s
"

# Simulate datasets to check the model
# Set `parallel` to TRUE for faster, usually much faster, analysis
# Set `progress` to TRUE to display the progress of the analysis

sim_only <- power4test(nrep = 10,
                       model = model_simple_med,
                       pop_es = model_simple_med_es,
                       n = 50,
                       fit_model_args = list(fit_function = "lm"),
                       do_the_test = FALSE,
                       iseed = 1234,
                       parallel = FALSE,
                       progress = FALSE)

# By n: Do a test for different sample sizes

out1 <- power4test_by_n(sim_only,
                        nrep = 10,
                        test_fun = test_parameters,
                        test_args = list(par = "y~x"),
                        n = c(25, 50, 100),
                        by_seed = 1234,
                        parallel = FALSE,
                        progress = FALSE)

pout1 <- power_curve(out1)
pout1
plot(pout1)

# By pop_es: Do a test for different population values of a model parameter

out2 <- power4test_by_es(sim_only,
                         nrep = 10,
                         test_fun = test_parameters,
                         test_args = list(par = "y~x"),
                         pop_es_name = "y ~ x",
                         pop_es_values = c(0, .3, .5),
                         by_seed = 1234,
                         parallel = FALSE,
                         progress = FALSE)

pout2 <- power_curve(out2)
pout2
plot(pout2)

Predict Method for a 'power_curve' Object

Description

Compute the predicted values in a model fitted by power_curve().

Usage

## S3 method for class 'power_curve'
predict(object, newdata, ...)

Arguments

Details

The predict method of power_curve objects works in two modes.

If new data is not supplied (through newdata), it retrieves the stored results and calls the corresponding methods to compute the predicted values, which are the predicted rejection rates (power levels if the null hypothesis is false, e.g., the population effect size is equal to zero).

If new data is supplied, such as a named list with a vector of sample sizes, they will be used to compute the predicted rejection rates.

Value

It returns a numeric vector of the predicted rejection rates.

See Also

power_curve().

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: s
"

# Simulate datasets to check the model
# Set `parallel` to TRUE for faster, usually much faster, analysis
# Set `progress` to TRUE to display the progress of the analysis

sim_only <- power4test(nrep = 10,
                       model = model_simple_med,
                       pop_es = model_simple_med_es,
                       n = 50,
                       fit_model_args = list(fit_function = "lm"),
                       do_the_test = FALSE,
                       iseed = 1234,
                       parallel = FALSE,
                       progress = FALSE)

# By n: Do a test for different sample sizes

out1 <- power4test_by_n(sim_only,
                        nrep = 10,
                        test_fun = test_parameters,
                        test_args = list(par = "y~x"),
                        n = c(25, 100, 200, 1000),
                        by_seed = 1234,
                        parallel = FALSE,
                        progress = FALSE)

pout1 <- power_curve(out1)
pout1
predict(pout1,
        newdata = list(x = c(150, 250, 500)))

# By pop_es: Do a test for different population values of a model parameter

out2 <- power4test_by_es(sim_only,
                             nrep = 10,
                             test_fun = test_parameters,
                             test_args = list(par = "y~x"),
                             pop_es_name = "y ~ x",
                             pop_es_values = seq(0, .7, .15),
                             by_seed = 1234,
                             parallel = FALSE,
                             progress = FALSE)

pout2 <- power_curve(out2)
pout2
predict(pout2,
        newdata = list(x = c(.25, .55)))

Generate the Population Model

Description

Generate the complete population model using the model syntax and user-specified effect sizes (population parameter values).

Usage

ptable_pop(
  model = NULL,
  pop_es = NULL,
  es1 = c(n = 0, nil = 0, s = 0.1, m = 0.3, l = 0.5, si = 0.141, mi = 0.361, li = 0.51),
  es2 = c(n = 0, nil = 0, s = 0.05, m = 0.1, l = 0.15),
  es_ind = c("si", "mi", "li"),
  standardized = TRUE,
  n_std = 1e+05,
  std_force_monte_carlo = FALSE,
  add_cov_for_moderation = TRUE
)

model_matrices_pop(x, ..., drop_list_single_group = TRUE)

Arguments

Details

The function ptable_pop() generates a lavaan parameter table that can be used to generate data based on the population values of model parameters.

Value

The function ptable_pop() returns a lavaan parameter table of the model, with the column start set to the population values.

The function model_matrices_pop() returns a lavaan LISREL-style model matrices (like the output of lavaan::lavInspect() with what set to "free"), with matrix elements set to the population values. If x is the model syntax, it will be stored in the attributes model. If the model is a multigroup model with k groups (k greater than 1), then it returns a list of k lists of lavaan LISREL-style model matrices unless drop_list_single_group is TRUE.

The role of ptable_pop()

The function ptable_pop() is used by the all-in-one function power4test(). Users usually do not call this function directly, though developers can use this function to develop other functions for power analysis, or to build their own workflows to do the power analysis.

Specify the Population Model by 'model'

Single-Group Model

For a single-group model, model should be a lavaan model syntax string of the form of the model. The population values of the model parameters are to be determined by pop_es.

If the model has latent factors, the syntax in model should specify only the structural model for the latent factors. There is no need to specify the measurement part. Other functions will generate the measurement part on top of this model.

For example, this is a simple mediation model:

"m ~ x
 y ~ m + x"

Whether m, x, and y denote observed variables or latent factors are determined by other functions, such as power4test().

Multigroup Model

Because the model is the population model, equality constraints are irrelevant and the model syntax specifies only the form of the model. Therefore, model is specified as in the case of single group models.

Specify 'pop_es' Using Named Vectors

The argument pop_es is for specifying the population values of model parameters. This section describes how to do this using named vectors.

Single-Group Model

If pop_es is specified by a named vector, it must follow the convention below.

• The names of the vectors are lavaan names for the selected parameters. For example, m ~ x denotes the path from x to m.

• Alternatively, the names can be either ".beta." or ".cov.". Use ".beta." to set the default values for all regression coefficients. Use ".cov." to set the default values for all correlations of exogenous variables (e.g., predictors).

• The names can also be of this form: ".ind.(<path>)", whether ⁠<path>⁠ denote path in the model. For example, ".ind.(x->m->y)" denotes the path from x through m to y. Alternatively, the lavaan symbol ~ can also be used: ".ind.(y~m~x)". This form is used to set the indirect effect (standardized, by default) along this path. The value for this name will override other settings.

• If using lavaan names, can specify more than one parameter using +. For example, y ~ m + x denotes the two paths from m and x to y.

• The value of each element can be the label for the effect size: n for nil, s for small, m for medium, and l for large. The value for each label is determined by es1 and es2. See the section on specifying these two arguments.

• The value of pop_es can also be set to a value, but it must be quoted as a string, such as "y ~ x" = ".31".

This is an example:

c(".beta." = "s",
  "m1 ~ x" = "-m",
  "m2 ~ m1" = "l",
  "y ~ x:w" = "s")

In this example,

• All regression coefficients are set to small (s) by default, unless specified otherwise.

• The path from x to m1 is set to medium and negative (-m).

• The path from m1 to m2 is set to large (l).

• The coefficient of the product term x:w when predicting y is set to small (s).

Indirect Effect

When setting an indirect effect to a symbol (default: "si", "mi", "li", with "i" added to differentiate them from the labels for a direct path), the corresponding value is used to determine the population values of all component paths along the pathway. All the values are assumed to be equal. Therefore, ".ind.(x->m->y)" = ".20" is equivalent to setting m ~ x and y ~ m to the square root of .20, such that the corresponding indirect effect is equal to the designated value.

This behavior, though restricted, is for quick manipulation of the indirect effect. If different values along a pathway, set the value for each path directly.

Only nonnegative value is supported. Therefore, ".ind.(x->m->y)" = "-si" and ".ind.(x->m->y)" = "-.20" will throw an error.

Multigroup Model

The argument pop_es also supports multigroup models.

For pop_es, instead of named vectors, named list of named vectors should be used.

• The names are the parameters, or keywords such as .beta. and .cov., like specifying the population values for a single group model.

• The elements are character vectors. If it has only one element (e.g., a single string), then it is the the population value for all groups. If it has more than one element (e.g., a vector of three strings), then they are the population values of the groups. For a model of k groups, each vector must have either k elements or one element.

This is an example:

list("m ~ x" = "m",
     "y ~ m" = c("s", "m", "l"))

In this model, the population value of the path m ~ x is medium (m) for all groups, while the population values for the path y ~ m are small (s), medium (m), and large (l), respectively.

Specify 'pop_es' Using a Multiline String

When setting the argument pop_es, instead of using a named vector or named list for pop_es, the population values of model parameters can also be specified using a multiline string, as illustrated below, to be parsed by pop_es_yaml().

Single-Group Model

This is an example of the multiline string for a single-group model:

y ~ m: l
m ~ x: m
y ~ x: nil

The string must follow this format:

• Each line starts with ⁠tag:⁠.

  • tag can be the name of a parameter, in lavaan model syntax format.

    • For example, m ~ x denotes the path from x to m.

  • A tag in lavaan model syntax can specify more than one parameter using +.

    • For example, y ~ m + x denotes the two paths from m and x to y.

  • Alternatively, the tag can be either .beta. or .cov..

    • Use .beta. to set the default values for all regression coefficients.

    • Use .cov. to set the default values for all correlations of exogenous variables (e.g., predictors).

• After each tag is the value of the population value:

  -nil for nil (zero),

  • s for small,

  • m for medium, and

  • l for large.

  • si, mi, and li for small, medium, and large a standardized indirect effect, respectively.

  Note: n cannot be used in this mode.

  The value for each label is determined by es1 and es2 as described in ptable_pop().

  • The value can also be set to a numeric value, such as .30 or -.30.

This is another example:

.beta.: s
y ~ m: l

In this example, all regression coefficients are small, while the path from m to y is large.

Multigroup Model

This is an example of the string for a multigroup model:

y ~ m: l
m ~ x:
  - nil
  - s
y ~ x: nil

The format is similar to that for a single-group model. If a parameter has the same value for all groups, then the line can be specified as in the case of a single-group model: tag: value.

If a parameter has different values across groups, then it must be in this format:

• A line starts with the tag, followed by two or more lines. Each line starts with a hyphen - and the value for a group.

For example:

m ~ x:
  - nil
  - s

This denotes that the model has two groups. The values of the path from x to m for the two groups are 0 (nil) and small (s), respectively.

Another equivalent way to specify the values are using ⁠[]⁠, on the same line of a tag.

For example:

m ~ x: [nil, s]

The number of groups is inferred from the number of values for a parameter. Therefore, if a tag has more than one value, each tag must has the same number of value, or only one value.

The tag .beta. and .cov. can also be used for multigroup models.

Which Approach To Use

Note that using named vectors or named lists is more reliable. However, using a multiline string is more user-friendly. If this method failed, please use named vectors or named list instead.

Technical Details

The multiline string is parsed by yaml::read_yaml(). Therefore, the format requirement is actually that of YAML. Users knowledgeable of YAML can use other equivalent way to specify the string.

Set the Values for Effect Size Labels ('es1' and 'es2')

The vector es1 is for correlations, regression coefficients, and indirect effect, and the vector es2 is for for standardized moderation effect, the coefficients of a product term. These labels are to be used in interpreting the specification in pop_es.

The role of model_matrices_pop()

The function model_matrices_pop() generates models matrices with population values, used by ptable_pop(). Users usually do not call this function directly, though developers can use this build their own workflows to generate the data.

See Also

power4test(), and pop_es_yaml() on an alternative way to specify population values.

Examples

# Specify the model

model1 <-
"
m1 ~ x + c1
m2 ~ m1 + x2 + c1
y ~  m2 + m1 + x + w + x:w + c1
"

# Specify the population values

model1_es <- c("m1 ~ x" = "-m",
               "m2 ~ m1" = "s",
               "y ~ m2" = "l",
               "y ~ x" = "m",
               "y ~ w" = "s",
               "y ~ x:w" = "s",
               "x ~~ w" = "s")

ptable_final1 <- ptable_pop(model1,
                            pop_es = model1_es)
ptable_final1

# Use a multiline string, illustrated by a simpler model

model2 <-
"
m ~ x
y ~ m + x
"

model2_es_a <- c("m ~ x" = "s",
                 "y ~ m" = "m",
                 "y ~ x" = "nil")

model2_es_b <-
"
m ~ x: s
y ~ m: m
y ~ x: nil
"

ptable_model2_a <- ptable_pop(model2,
                              pop_es = model2_es_a)
ptable_model2_b <- ptable_pop(model2,
                              pop_es = model2_es_b)

ptable_model2_a
ptable_model2_b

identical(ptable_model2_a,
          ptable_model2_b)

# model_matrices_pop

model_matrices_pop(ptable_final1)

model_matrices_pop(model1,
                   pop_es = model1_es)

Random Variable From a Beta Distribution

Description

Generate random numbers from a beta distribution, rescaled to have user-specified population mean and standard deviation.

Usage

rbeta_rs(n = 10, shape1 = 0.5, shape2 = 0.5, pmean = 0, psd = 1)

Arguments

Details

First, specify the two parameters, shape1 and shape2, and the desired population mean and standard deviation. The random numbers, drawn from a beta distribution by stats::rbeta() will then be rescaled with the desired population mean and standard deviation.

Value

A vector of the generated random numbers.

Examples

set.seed(90870962)
x <- rbeta_rs(n = 5000,
              shape1 = .5,
              shape2 = .5,
              pmean = 3,
              psd = 1)
mean(x)
sd(x)
hist(x)

Random Variable From a Beta Distribution (User Range)

Description

Generate random numbers from a beta distribution, rescaled to have user-specified population mean and standard deviation, and within a specific range.

Usage

rbeta_rs2(n = 10, bmean, bsd, blow = 0, bhigh = 1)

Arguments

Details

First, specify the two parameters, shape1 and shape2, and the desired population mean and standard deviation. The random numbers, drawn from a beta distribution by stats::rbeta() will then be rescaled to the desired population range.

Value

A vector of the generated random numbers.

Examples

set.seed(90870962)
x <- rbeta_rs2(n = 5000,
               bmean = .80,
               bsd = .10,
               blow = .00,
               bhigh = .95)
mean(x)
sd(x)
hist(x)
y <- rbeta_rs2(n = 5000,
               bmean = 4,
               bsd = 3,
               blow = -10,
               bhigh = 10)
mean(y)
sd(y)
hist(y)

Random Binary Variable

Description

Generate random numbers from a distribution of 0 or 1, rescaled to have user-specified population mean and standard deviation.

Usage

rbinary_rs(n = 10, p1 = 0.5, pmean = 0, psd = 1)

Arguments

Details

First, specify probability of 1 (p1), and the desired population mean and standard deviation. The random numbers, drawn from a distribution of 0 (1 - p1 probability) and 1 (p1 probability), will then be rescaled with the desired population mean and standard.

Value

A vector of the generated random numbers.

Examples

set.seed(90870962)
x <- rbinary_rs(n = 5000,
                p1 = .5,
                pmean = 3,
                psd = 1)
mean(x)
sd(x)
hist(x)

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

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.

Random Variable From an Exponential Distribution

Description

Generate random numbers from an exponential distribution, rescaled to have user-specified population mean and standard deviation.

Usage

rexp_rs(n = 10, rate = 1, pmean = 0, psd = 1, rev = FALSE)

Arguments

Details

First, specify the parameter, rate, and the desired population mean and standard deviation. The random numbers, drawn from an exponential distribution by stats::rexp(), will then be rescaled with the desired population mean and standard.

Value

A vector of the generated random numbers.

Examples

set.seed(90870962)
x <- rexp_rs(n = 5000,
             rate = 4,
             pmean = 3,
             psd = 1)
mean(x)
sd(x)
hist(x)

Random Variable From a Lognormal Distribution

Description

Generate random numbers from a lognormal distribution, rescaled to have user-specified population mean and standard deviation.

Usage

rlnorm_rs(n = 10, mui = 0, sigma = 1, rev = FALSE, pmean = 0, psd = 1)

Arguments

Details

First, specify the parameter, mui and sigma, and the desired population mean and standard deviation. The random numbers, drawn from a lognormal distribution by stats::rlnorm(), will then be rescaled with the desired population mean and standard.

Value

A vector of the generated random numbers.

Examples

set.seed(90870962)
x <- rlnorm_rs(n = 5000,
               mui = 0,
               sigma = 1,
               pmean = 0,
               psd = 1)
mean(x)
sd(x)
hist(x)

Random Variable From a Generalized Normal Distribution

Description

Generate random numbers from generalized normal distribution, rescaled to have user-specified population mean and standard deviation.

Usage

rpgnorm_rs(n = 10, p = 2, pmean = 0, psd = 1)

Arguments

Details

First, specify the parameter p and the desired population mean and standard deviation. The random numbers, drawn from a generalized normal distribution by pgnorm::rpgnorm(), will then be rescaled with the desired population mean and standard.

Value

A vector of the generated random numbers.

Examples

set.seed(90870962)
x <- rpgnorm_rs(n = 5000,
                p = 2,
                pmean = 0,
                psd = 1)
mean(x)
sd(x)
hist(x)
x_kurt <- function(p) {gamma(5/p)*gamma(1/p)/(gamma(3/p)^2) - 3}

p <- 6
x <- rpgnorm_rs(n = 50000,
                p = p,
                pmean = 0,
                psd = 1)
mean(x)
sd(x)
x_kurt(p)
qqnorm(x); qqline(x)

p <- 1
x <- rpgnorm_rs(n = 50000,
                p = p,
                pmean = 0,
                psd = 1)
mean(x)
sd(x)
x_kurt(p)
qqnorm(x); qqline(x)

Random Variable From a t Distribution

Description

Generate random numbers from a t distribution, rescaled to have user-specified population mean and standard deviation.

Usage

rt_rs(n = 10, df = 5, pmean = 0, psd = 1)

Arguments

Details

First, specify the parameter df and the desired population mean and standard deviation. The random numbers, drawn from the generalized normal distribution by stats::rt(), will then be rescaled with the desired population mean and standard.

Value

A vector of the generated random numbers.

Examples

set.seed(90870962)
x <- rt_rs(n = 5000,
           df = 5,
           pmean = 3,
           psd = 1)
mean(x)
sd(x)
hist(x)

Random Variable From a Uniform Distribution

Description

Generate random numbers from a uniform distribution, with user-specified population mean and standard deviation.

Usage

runif_rs(n = 10, min = 0, max = 1, pmean = 0, psd = 1)

Arguments

Details

First, the user specifies the parameters, min and max, and the desired population mean and standard deviation. Then the random numbers will be generated and rescaled with the desired population mean and standard.

Value

A vector of the generated random numbers.

Examples

set.seed(90870962)
x <- runif_rs(n = 5000,
              min = 2,
              max = 4,
              pmean = 3,
              psd = 1)
mean(x)
sd(x)
hist(x)

Simulate Datasets Based on a Model

Description

Get a model matrix and effect size specification and simulate a number of datasets, along with other information.

The function

Usage

sim_data(
  nrep = 10,
  ptable = NULL,
  model = NULL,
  pop_es = NULL,
  ...,
  n = 100,
  iseed = NULL,
  number_of_indicators = NULL,
  reliability = NULL,
  x_fun = list(),
  e_fun = list(),
  process_data = NULL,
  parallel = FALSE,
  progress = FALSE,
  ncores = max(1, parallel::detectCores(logical = FALSE) - 1)
)

## S3 method for class 'sim_data'
print(
  x,
  digits = 3,
  digits_descriptive = 2,
  data_long = TRUE,
  fit_to_all_args = list(),
  est_type = "standardized",
  variances = NULL,
  pure_x = TRUE,
  pure_y = TRUE,
  ...
)

pool_sim_data(object, as_list = FALSE)

Arguments

Details

The function sim_data() generates a list of datasets based on a population model.

Value

The function sim_out() returns a list of the class sim_data, with length nrep. Each element is a sim_data_i object, with the following major elements:

• ptable: A lavaan parameter table of the model, with population values set in the column start. (It is the output of the function ptable_pop().)

• mm_out: The population model represented by model matrices as in lavaan. (It is the output of the function model_matrices_pop().)

• mm_lm_out: A list of regression model formula, one for each endogenous variable. (It is the output of the internal function mm_lm().)

• mm_lm_dat_out: A simulated dataset generated from the population model. (It is the output of the internal function mm_lm_data()).

• model_original: The original model syntax (i.e., the argument model).

• model_final: A modified model syntax if the model is a latent variable model. Indicators are added to the syntax.

• fit0: The output of lavaan::sem() with ptable as the model and do.fit set to FALSE. Used for the easy retrieval of information about the model.

The print method of sim_data returns x invisibly. It is called for its side effect.

The function pool_sim_data() returns either one data frame or a list of data frames, depending on the argument as_list

The role of sim_data()

The function sim_data() is used by the all-in-one function power4test(). Users usually do not call this function directly, though developers can use this function to develop other functions for power analysis, or to build their own workflows to do the power analysis.

Workflow

The function sim_data() does two tasks:

• Determine the actual population model with population values based on:

  • A model syntax for the observed variables (for a path model) or the latent factors (for a latent variable model).

  • A textual specification of the effect sizes of parameters.

  • The number of indicators for each latent factor if the model is a latent variable model.

  • The reliability of each latent factor as measured by the indicators if the model is a latent factor model.

• Generate nrep simulated datasets from the population model.

The simulated datasets can then be used to fit a model, test parameters, and estimate power.

The output is usually used by fit_model() to fit a target model, by default the population model, to each of the dataset.

Set 'number_of_indicators' and 'reliability'

The arguments number_of_indicators and reliability are used to specify the number of indicators (e.g., items) for each factor, and the population reliability coefficient of each factor, if the variables in the model syntax are latent variables.

Single-Group Model

If a variable in the model is to be replaced by indicators in the generated data, set number_of_indicators to a named numeric vector. The names are the variables of variables with indicators, as appeared in the model syntax. The value of each name is the number of indicators.

The argument reliability should then be set a named numeric vector (or list, see the section on multigroup models) to specify the population reliability coefficient ("omega") of each set of indicators. The population standardized factor loadings are then computed to ensure that the population reliability coefficient is of the target value.

These are examples for a single group model:

number of indicator = c(m = 3, x = 4, y = 5)

The numbers of indicators for m, x, and y are 3, 4, and 5, respectively.

reliability = c(m = .90, x = .80, y = .70)

The population reliability coefficients of m, x, and y are .90, .80, and .70, respectively.

Multigroup Models

Multigroup models are supported. The number of groups is inferred from pop_es (see the help page of ptable_pop()), or directly from ptable.

For a multigroup model, the number of indicators for each variable must be the same across groups.

However, the population reliability coefficients can be different across groups. For a multigroup model of k groups, with one or more population reliability coefficients differ across groups, the argument reliability should be set to a named list. The names are the variables to which the population reliability coefficients are to be set. The element for each name is either a single value for the common reliability coefficient, or a numeric vector of the reliability coefficient of each group.

This is an example of reliability for a model with 2 groups:

reliability = list(x = .80, m = c(.70, .80))

The reliability coefficients of x are .80 in all groups, while the reliability coefficients of m are .70 in one group and .80 in another.

Equal Numbers of Indicators and/or Reliability Coefficients

If all variables in the model has the same number of indicators, number_of_indicators can be set to one single value.

Similarly, if all sets of indicators have the same population reliability in all groups, reliability can also be set to one single value.

Specify The Distributions of Exogenous Variables Or Error Terms Using 'x_fun'

By default, variables and error terms are generated from a multivariate normal distribution. If desired, users can supply the function used to generate an exogenous variable and error term by setting x_fun to a named list.

The names of the list are the variables for which a user function will be used to generate the data.

Each element of the list must also be a list. The first element of this list, can be unnamed, is the function to be used. If other arguments need to be supplied, they should be included as named elements of this list.

For example:

x_fun = list(x = list(power4mome::rexp_rs),
             w = list(power4mome::rbinary_rs,
                      p1 = .70)))

The variables x and w will be generated by user-supplied functions.

For x, the function is power4mome::rexp_rs. No additional argument when calling this function.

For w, the function is power4mome::rbinary_rx. The argument p1 = .70 will be passed to this function when generating the values of w.

If a variable is an endogenous variable (e.g., being predicted by another variable in a model), then x_fun is used to generate its error term. Its implied population distribution may still be different from that generate by x_fun because the distribution also depends on the distribution of other variables predicting it.

These are requirements for the user-functions:

• They must return a numeric vector.

• They mush has an argument n for the number of values.

• The population mean and standard deviation of the generated values must be 0 and 1, respectively.

The package power4mome has helper functions for generating values from some common nonnormal distributions and then scaling them to have population mean and standard deviation equal to 0 and 1 (by default), respectively. These are some of them:

• rbinary_rs().

• rexp_rs().

• rbeta_rs().

• rlnorm_rs().

• rpgnorm_rs().

To use x_fun, the variables must have zero covariances with other variables in the population. It is possible to generate nonnormal multivariate data but we believe this is rarely needed when estimating power before having the data.

Specify the Population Model by 'model'

Single-Group Model

For a single-group model, model should be a lavaan model syntax string of the form of the model. The population values of the model parameters are to be determined by pop_es.

If the model has latent factors, the syntax in model should specify only the structural model for the latent factors. There is no need to specify the measurement part. Other functions will generate the measurement part on top of this model.

For example, this is a simple mediation model:

"m ~ x
 y ~ m + x"

Whether m, x, and y denote observed variables or latent factors are determined by other functions, such as power4test().

Multigroup Model

Because the model is the population model, equality constraints are irrelevant and the model syntax specifies only the form of the model. Therefore, model is specified as in the case of single group models.

Specify 'pop_es' Using Named Vectors

The argument pop_es is for specifying the population values of model parameters. This section describes how to do this using named vectors.

Single-Group Model

If pop_es is specified by a named vector, it must follow the convention below.

• The names of the vectors are lavaan names for the selected parameters. For example, m ~ x denotes the path from x to m.

• Alternatively, the names can be either ".beta." or ".cov.". Use ".beta." to set the default values for all regression coefficients. Use ".cov." to set the default values for all correlations of exogenous variables (e.g., predictors).

• The names can also be of this form: ".ind.(<path>)", whether ⁠<path>⁠ denote path in the model. For example, ".ind.(x->m->y)" denotes the path from x through m to y. Alternatively, the lavaan symbol ~ can also be used: ".ind.(y~m~x)". This form is used to set the indirect effect (standardized, by default) along this path. The value for this name will override other settings.

• If using lavaan names, can specify more than one parameter using +. For example, y ~ m + x denotes the two paths from m and x to y.

• The value of each element can be the label for the effect size: n for nil, s for small, m for medium, and l for large. The value for each label is determined by es1 and es2. See the section on specifying these two arguments.

• The value of pop_es can also be set to a value, but it must be quoted as a string, such as "y ~ x" = ".31".

This is an example:

c(".beta." = "s",
  "m1 ~ x" = "-m",
  "m2 ~ m1" = "l",
  "y ~ x:w" = "s")

In this example,

• All regression coefficients are set to small (s) by default, unless specified otherwise.

• The path from x to m1 is set to medium and negative (-m).

• The path from m1 to m2 is set to large (l).

• The coefficient of the product term x:w when predicting y is set to small (s).

Indirect Effect

When setting an indirect effect to a symbol (default: "si", "mi", "li", with "i" added to differentiate them from the labels for a direct path), the corresponding value is used to determine the population values of all component paths along the pathway. All the values are assumed to be equal. Therefore, ".ind.(x->m->y)" = ".20" is equivalent to setting m ~ x and y ~ m to the square root of .20, such that the corresponding indirect effect is equal to the designated value.

This behavior, though restricted, is for quick manipulation of the indirect effect. If different values along a pathway, set the value for each path directly.

Only nonnegative value is supported. Therefore, ".ind.(x->m->y)" = "-si" and ".ind.(x->m->y)" = "-.20" will throw an error.

Multigroup Model

The argument pop_es also supports multigroup models.

For pop_es, instead of named vectors, named list of named vectors should be used.

• The names are the parameters, or keywords such as .beta. and .cov., like specifying the population values for a single group model.

• The elements are character vectors. If it has only one element (e.g., a single string), then it is the the population value for all groups. If it has more than one element (e.g., a vector of three strings), then they are the population values of the groups. For a model of k groups, each vector must have either k elements or one element.

This is an example:

list("m ~ x" = "m",
     "y ~ m" = c("s", "m", "l"))

In this model, the population value of the path m ~ x is medium (m) for all groups, while the population values for the path y ~ m are small (s), medium (m), and large (l), respectively.

Specify 'pop_es' Using a Multiline String

When setting the argument pop_es, instead of using a named vector or named list for pop_es, the population values of model parameters can also be specified using a multiline string, as illustrated below, to be parsed by pop_es_yaml().

Single-Group Model

This is an example of the multiline string for a single-group model:

y ~ m: l
m ~ x: m
y ~ x: nil

The string must follow this format:

• Each line starts with ⁠tag:⁠.

  • tag can be the name of a parameter, in lavaan model syntax format.

    • For example, m ~ x denotes the path from x to m.

  • A tag in lavaan model syntax can specify more than one parameter using +.

    • For example, y ~ m + x denotes the two paths from m and x to y.

  • Alternatively, the tag can be either .beta. or .cov..

    • Use .beta. to set the default values for all regression coefficients.

    • Use .cov. to set the default values for all correlations of exogenous variables (e.g., predictors).

• After each tag is the value of the population value:

  -nil for nil (zero),

  • s for small,

  • m for medium, and

  • l for large.

  • si, mi, and li for small, medium, and large a standardized indirect effect, respectively.

  Note: n cannot be used in this mode.

  The value for each label is determined by es1 and es2 as described in ptable_pop().

  • The value can also be set to a numeric value, such as .30 or -.30.

This is another example:

.beta.: s
y ~ m: l

In this example, all regression coefficients are small, while the path from m to y is large.

Multigroup Model

This is an example of the string for a multigroup model:

y ~ m: l
m ~ x:
  - nil
  - s
y ~ x: nil

The format is similar to that for a single-group model. If a parameter has the same value for all groups, then the line can be specified as in the case of a single-group model: tag: value.

If a parameter has different values across groups, then it must be in this format:

• A line starts with the tag, followed by two or more lines. Each line starts with a hyphen - and the value for a group.

For example:

m ~ x:
  - nil
  - s

This denotes that the model has two groups. The values of the path from x to m for the two groups are 0 (nil) and small (s), respectively.

Another equivalent way to specify the values are using ⁠[]⁠, on the same line of a tag.

For example:

m ~ x: [nil, s]

The number of groups is inferred from the number of values for a parameter. Therefore, if a tag has more than one value, each tag must has the same number of value, or only one value.

The tag .beta. and .cov. can also be used for multigroup models.

Which Approach To Use

Note that using named vectors or named lists is more reliable. However, using a multiline string is more user-friendly. If this method failed, please use named vectors or named list instead.

Technical Details

The multiline string is parsed by yaml::read_yaml(). Therefore, the format requirement is actually that of YAML. Users knowledgeable of YAML can use other equivalent way to specify the string.

Set the Values for Effect Size Labels ('es1' and 'es2')

The vector es1 is for correlations, regression coefficients, and indirect effect, and the vector es2 is for for standardized moderation effect, the coefficients of a product term. These labels are to be used in interpreting the specification in pop_es.

See Also

power4test()

Examples

# Specify the model

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

# Specify the population values

es <-
"
y ~ m: m
m ~ x: m
y ~ x: n
"

# Generate the simulated datasets

data_all <- sim_data(nrep = 5,
                     model = mod,
                     pop_es = es,
                     n = 100,
                     iseed = 1234)

data_all

Create a 'sim_out' Object

Description

Combine the outputs of sim_data(), fit_model(), and optionally gen_mc() and/or gen_boot() to one single object.

Usage

sim_out(data_all, ...)

## S3 method for class 'sim_out'
print(x, digits = 3, digits_descriptive = 2, fit_to_all_args = list(), ...)

Arguments

Details

It merges into one object the output of sim_data(), which is a list of nrep simulated datasets, fit_model(), which is a list of the lavaan::sem() output for the nrep datasets, and optionally the output of gen_mc() or gen_boot(), which is a list of the R sets of Monte Carlo or bootstrap estimates based on the results of fit_model(). The list has nrep elements, each element with the data, the model fit results, and optionally the Monte Carlo estimates matched.

This object can then be used for testing effects of interests, which are further processed to estimate the power of this test.

The function sim_out() is used by the all-in-one function power4test(). Users usually do not call this function directly, though developers can use this function to develop other functions for power analysis, or to build their own workflows to do the power analysis.

Value

The function sim_out() returns a sim_out object, which is a list of length equal to the length of data_all. Each element of the list is a sim_data object with the element extra added to it. Other named elements will be added under this name. For example. the output of fit_model() for this replication can be added to fit, under extra. See the description of the argument ... for details.

The print method of sim_out returns x invisibly. Called for its side effect.

See Also

power4test()

Examples

# Specify the model

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

# Specify the population values

es <-
"
y ~ m: m
m ~ x: m
y ~ x: n
"

# Generate the simulated datasets

dats <- sim_data(nrep = 5,
                 model = mod,
                 pop_es = es,
                 n = 100,
                 iseed = 1234)

# Fit the population model to each dataset

fits <- fit_model(dats)

# Combine the results to one object

sim_out_all <- sim_out(data_all = dats,
                       fit = fits)
sim_out_all

# Verify that the elements of fits are set to extra$fit

library(lavaan)
parameterEstimates(fits[[1]])
parameterEstimates(sim_out_all[[1]]$extra$fit)
parameterEstimates(fits[[2]])
parameterEstimates(sim_out_all[[2]]$extra$fit)

Summarize Test Results

Description

Extract and summarize test results.

Usage

summarize_tests(
  object,
  collapse = c("none", "all_sig", "at_least_one_sig", "at_least_k_sig"),
  at_least_k = 1
)

## S3 method for class 'test_summary_list'
print(x, digits = 3, ...)

## S3 method for class 'test_summary'
print(x, digits = 2, ...)

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

Arguments

Details

The function summarize_tests() is used to extract information from each test stored in a power4test object.

The method print.test_out_list() is used to print the content of a list of test stored in a power4test object, with the option to print just the names of tests.

Value

The function summarize_tests() returns a list of the class test_summary_list. Each element contains a summary of a test stored. The elements are of the class test_summary, with these elements:

• test_attributes: The stored information of a test, for printing.

• nrep: The number of datasets (replications).

• mean: The means of numeric information. For significance tests, these are the rejection rates.

• nvalid: The number of non-NA replications used to compute each mean.

The print methods returns x invisibly. They are called for their side effects.

The role of summarize_tests() and related functions

The function summarize_tests() and related print methods are used by the all-in-one function power4test() and its summary method. Users usually do not call them directly, though developers can use this function to develop other functions for power analysis, or to build their own workflows to do the power analysis.

See Also

power4test()

Examples

# Specify the model

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

# Specify the population values

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

# Simulated datasets

sim_only <- power4test(nrep = 2,
                       model = mod,
                       pop_es = es,
                       n = 100,
                       do_the_test = FALSE,
                       iseed = 1234)

# Test the parameters in each dataset

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

# Print the summary

summarize_tests(test_out)

Summarize 'x_from_power' Results

Description

The summary method of the output of x_from_power().

Usage

## S3 method for class 'x_from_power'
summary(object, ...)

## S3 method for class 'n_region_from_power'
summary(object, ...)

## S3 method for class 'summary.x_from_power'
print(x, digits = 3, ...)

## S3 method for class 'summary.n_region_from_power'
print(x, digits = 3, ...)

Arguments

Details

The summary method simply prepares the results of x_from_power() to be printed in details.

Value

The summary method for x_from_power objects returns an object of the class summary.x_from_power, which is simply the output of x_from_power(), with a print method dedicated for detailed summary. Please refer to x_from_power() for the contents.

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

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

See Also

x_from_power(), n_region_from_power()

Examples

# Specify the population model

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

# Specify the population values

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

# Generate the datasets

sim_only <- power4test(nrep = 5,
                       model = mod,
                       pop_es = mod_es,
                       n = 100,
                       do_the_test = FALSE,
                       iseed = 2345)

# Do a test

test_out <- power4test(object = sim_only,
                       test_fun = test_parameters,
                       test_args = list(pars = "m~x"))

# Determine the sample size with a power of .80 (default)

power_vs_n <- x_from_power(test_out,
                           x = "n",
                           progress = TRUE,
                           target_power = .80,
                           final_nrep = 5,
                           max_trials = 1,
                           seed = 1234)
summary(power_vs_n)

Test a Conditional Indirect Effect

Description

Test a conditional indirect effect for a power4test object.

Usage

test_cond_indirect(
  fit = fit,
  x = NULL,
  m = NULL,
  y = NULL,
  wvalues = NULL,
  mc_ci = TRUE,
  mc_out = NULL,
  boot_ci = FALSE,
  boot_out = NULL,
  check_post_check = TRUE,
  ...,
  fit_name = "fit",
  get_map_names = FALSE,
  get_test_name = FALSE
)

Arguments

Details

This function is to be used in power4test() for testing a conditional indirect effect, by setting it to the test_fun argument.

It uses manymome::cond_indirect() to do the test. It can be used on models fitted by lavaan::sem() or fitted by a sequence of calls to stats::lm(), although only nonparametric bootstrap confidence interval is supported for models fitted by regression using stats::lm().

It can also be used to test a conditional effect on a direct path with no mediator. Just omit m when calling the function.

Value

In its normal usage, it returns a named numeric vector with the following elements:

• est: The mean of the estimated indirect effect across datasets.

• cilo and cihi: The means of the lower and upper limits of the confidence interval (95% by default), respectively.

• sig: Whether a test by confidence interval is significant (1) or not significant (0).

See Also

power4test()

Examples

# Specify the model

model_simple_mod <-
"
m ~ x + w + x:w
y ~ m + x
"

# Specify the population values

model_simple_mod_es <-
"
y ~ m: l
y ~ x: n
m ~ x: m
m ~ w: n
m ~ x:w: l
"

# Simulate the data

sim_only <- power4test(nrep = 5,
                       model = model_simple_mod,
                       pop_es = model_simple_mod_es,
                       n = 100,
                       R = 100,
                       do_the_test = FALSE,
                       iseed = 1234)

# Do the test in each replication

test_ind <- power4test(object = sim_only,
                       test_fun = test_cond_indirect,
                       test_args = list(x = "x",
                                        m = "m",
                                        y = "y",
                                        wvalues = c(w = 1),
                                        mc_ci = TRUE))
print(test_ind,
      test_long = TRUE)

Test Several Conditional Indirect Effects

Description

Test several conditional indirect effects for a power4test object.

Usage

test_cond_indirect_effects(
  fit = fit,
  x = NULL,
  m = NULL,
  y = NULL,
  wlevels = NULL,
  mc_ci = TRUE,
  mc_out = NULL,
  boot_ci = FALSE,
  boot_out = NULL,
  check_post_check = TRUE,
  ...,
  fit_name = "fit",
  get_map_names = FALSE,
  get_test_name = FALSE
)

Arguments

Details

This function is to be used in power4test() for testing several conditional indirect effects, by setting it to the test_fun argument.

It uses manymome::cond_indirect_effects() to do the test. It can be used on models fitted by lavaan::sem() or fitted by a sequence of calls to stats::lm(), although only nonparametric bootstrap confidence interval is supported for models fitted by regression using stats::lm().

It can also be used to test conditional effects on a direct path with no mediator. Just omit m when calling the function.

Value

In its normal usage, it returns the output returned by manymome::cond_indirect_effects(), with the following modifications:

• est: The estimated conditional indirect effects.

• cilo and cihi: The lower and upper limits of the confidence interval (95% by default), respectively.

• sig: Whether a test by confidence interval is significant (1) or not significant (0).

• test_label: A column of labels generated to label the conditional effects.

See Also

power4test()

Examples

# Specify the model

model_simple_mod <-
"
m ~ x + w + x:w
y ~ m + x
"

# Specify the population values

model_simple_mod_es <-
"
y ~ m: l
y ~ x: n
m ~ x: m
m ~ w: n
m ~ x:w: l
"

# Simulate the data

# Set nrep to a larger value in real analysis, such as 400
sim_only <- power4test(nrep = 5,
                       model = model_simple_mod,
                       pop_es = model_simple_mod_es,
                       n = 100,
                       R = 100,
                       do_the_test = FALSE,
                       iseed = 1234)

# Do the tests in each replication

test_out <- power4test(object = sim_only,
                       test_fun = test_cond_indirect_effects,
                       test_args = list(x = "x",
                                        m = "m",
                                        y = "y",
                                        wlevels = c("w"),
                                        mc_ci = TRUE))
print(test_out,
      test_long = TRUE)

Test a Moderated Mediation Effect

Description

Test a moderated mediation effect for a power4test object.

Usage

test_index_of_mome(
  fit = fit,
  x = NULL,
  m = NULL,
  y = NULL,
  w = NULL,
  mc_ci = TRUE,
  mc_out = NULL,
  boot_ci = FALSE,
  boot_out = NULL,
  check_post_check = TRUE,
  ...,
  fit_name = "fit",
  get_map_names = FALSE,
  get_test_name = FALSE
)

Arguments

Details

This function is to be used in power4test() for testing a moderated mediation effect, by setting it to the test_fun argument.

It uses manymome::index_of_mome() to do the test. It can be used on models fitted by lavaan::sem() or fitted by a sequence of calls to stats::lm(), although only nonparametric bootstrap confidence interval is supported for models fitted by regression using stats::lm().

Value

In its normal usage, it returns a named numeric vector with the following elements:

• est: The mean of the estimated indirect effect across datasets.

• cilo and cihi: The means of the lower and upper limits of the confidence interval (95% by default), respectively.

• sig: Whether a test by confidence interval is significant (1) or not significant (0).

See Also

power4test()

Examples

# Specify the model

mod <-
"
m ~ x + w + x:w
y ~ m
"

# Specify the population values

mod_es <-
"
m ~ x: n
y ~ x: m
m ~ w: l
m ~ x:w: l
"

# Simulate the data

sim_only <- power4test(nrep = 2,
                       model = mod,
                       pop_es = mod_es,
                       n = 100,
                       R = 100,
                       do_the_test = FALSE,
                       iseed = 1234)

# Do the test in each replication

test_out <- power4test(object = sim_only,
                       test_fun = test_index_of_mome,
                       test_args = list(x = "x",
                                        m = "m",
                                        y = "y",
                                        w = "w",
                                        mc_ci = TRUE))

print(test_out,
      test_long = TRUE)

Test an Indirect Effect

Description

Test an indirect effect for a power4test object.

Usage

test_indirect_effect(
  fit = fit,
  x = NULL,
  m = NULL,
  y = NULL,
  mc_ci = TRUE,
  mc_out = NULL,
  boot_ci = FALSE,
  boot_out = NULL,
  check_post_check = TRUE,
  ...,
  fit_name = "fit",
  get_map_names = FALSE,
  get_test_name = FALSE
)

Arguments

Details

This function is to be used in power4test() for testing an indirect effect, by setting it to the test_fun argument.

It uses manymome::indirect_effect() to do the test. It can be used on models fitted by lavaan::sem() or fitted by a sequence of calls to stats::lm(), although only nonparametric bootstrap confidence interval is supported for models fitted by regression using stats::lm().

Value

In its normal usage, it returns a named numeric vector with the following elements:

• est: The mean of the estimated indirect effect across datasets.

• cilo and cihi: The means of the lower and upper limits of the confidence interval (95% by default), respectively.

• sig: Whether a test by confidence interval is significant (1) or not significant (0).

See Also

power4test()

Examples

# Specify the model

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

# Specify the population values

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

# Simulate the data

sim_only <- power4test(nrep = 5,
                       model = model_simple_med,
                       pop_es = model_simple_med_es,
                       n = 100,
                       R = 100,
                       do_the_test = FALSE,
                       iseed = 1234)

# Do the test in each replication

test_ind <- power4test(object = sim_only,
                       test_fun = test_indirect_effect,
                       test_args = list(x = "x",
                                        m = "m",
                                        y = "y",
                                        mc_ci = TRUE))
print(test_ind,
      test_long = TRUE)

Test Several Indirect Effects

Description

Test several indirect effects for a power4test object.

Usage

test_k_indirect_effects(
  fit = fit,
  x = NULL,
  m = NULL,
  y = NULL,
  mc_ci = TRUE,
  mc_out = NULL,
  boot_ci = FALSE,
  boot_out = NULL,
  check_post_check = TRUE,
  ...,
  omnibus = c("no", "all_sig", "at_least_one_sig", "at_least_k_sig"),
  at_least_k = 1,
  fit_name = "fit",
  get_map_names = FALSE,
  get_test_name = FALSE
)

Arguments

Details

This function is to be used in power4test() for testing an indirect effect, by setting it to the test_fun argument.

It uses manymome::many_indirect_effects() to do the test. It can be used on models fitted by lavaan::sem() or fitted by a sequence of calls to stats::lm(), although only nonparametric bootstrap confidence interval is supported for models fitted by regression using stats::lm().

Value

In its normal usage, it returns a data frame with the following columns:

• est: The estimated indirect effect for each path.

• cilo and cihi: The lower and upper limits of the confidence interval (95% by default), respectively, for each indirect effect

• sig: Whether a test by confidence interval is significant (1) or not significant (0).

• test_label: A column of labels generated to label the indirect effects.

If omnibus is "all_sig" or ⁠"at_least_one"sig"⁠, then the data frame has only one row, and the columns "est", "cilo", and "cihi" are NA. The column sig is determined by whether all paths are significant ("all_sig") or whether at least one path is significant ("at_least_one_sig").

See Also

power4test()

Examples

# Specify the model

model_simple_med <-
"
m1 ~ x
m2 ~ x
y ~ m1 + m2 + x
"

# Specify the population values

model_simple_med_es <-
"
y ~ m1: s
m1 ~ x: m
y ~ m2: s
m2 ~ x: l
y ~ x: n
"

# Simulate the data

sim_only <- power4test(nrep = 5,
                       model = model_simple_med,
                       pop_es = model_simple_med_es,
                       n = 100,
                       R = 100,
                       do_the_test = FALSE,
                       iseed = 1234)

# Do the test in each replication

test_ind <- power4test(object = sim_only,
                       test_fun = test_k_indirect_effects,
                       test_args = list(x = "x",
                                        y = "y",
                                        mc_ci = TRUE))
print(test_ind,
      test_long = TRUE)

# Set omnibus = "all_sig" to declare
# significant only if all paths are
# significant

test_ind_all_sig <- power4test(
                       object = sim_only,
                       test_fun = test_k_indirect_effects,
                       test_args = list(x = "x",
                                        y = "y",
                                        mc_ci = TRUE,
                                        omnibus = "all_sig"))
print(test_ind_all_sig,
      test_long = TRUE)

Test All Moderation Effects

Description

Test all moderation effects by testing all product terms for a power4test object.

Usage

test_moderation(
  fit = fit,
  standardized = FALSE,
  check_post_check = TRUE,
  ...,
  fit_name = "fit",
  get_map_names = FALSE,
  get_test_name = FALSE
)

Arguments

Details

This function is to be used in power4test() for testing all product terms, by setting it to the test_fun argument.

It is just a wrapper to test_parameters(). It will first identifies all product terms (terms with : in the names), and then call test_parameters(), with pars set to select the regression coefficients of these terms.

Value

In its normal usage, it returns the output returned by lavaan::parameterEstimates() or lmhelprs::lm_list_to_partable(), with the following modifications:

• est: The parameter estimates, even if standardized estimates are requested (not est.std).

• cilo and cihi: The lower and upper limits of the confidence interval (95% by default), respectively (not ci.lower and ci.upper).

• sig: Whether a test by confidence interval is significant (1) or not significant (0).

• test_label: A column of labels generated by lavaan::lav_partable_labels(), which are usually the labels used by coef() to label the parameters.

See Also

power4test(), test_parameters()

Examples

# Specify the model

mod <-
"
m ~ x + w1 + x:w1
y ~ m + x
"

# Specify the population values

mod_es <-
"
m ~ x: n
y ~ x: m
m ~ w1: n
m ~ x:w1: l
"

# Simulate the data

sim_only <- power4test(nrep = 4,
                       model = mod,
                       pop_es = mod_es,
                       n = 100,
                       do_the_test = FALSE,
                       iseed = 1234)

# Do the test in each replication

test_out <- power4test(object = sim_only,
                       test_fun = test_moderation)

print(test_out,
      test_long = TRUE)

Test All Free Parameters

Description

Test all free parameters, including user-defined parameters, for a power4test object.

Usage

test_parameters(
  fit = fit,
  standardized = FALSE,
  pars = NULL,
  op = NULL,
  remove.nonfree = TRUE,
  check_post_check = TRUE,
  ...,
  omnibus = c("no", "all_sig", "at_least_one_sig", "at_least_k_sig"),
  at_least_k = 1,
  fit_name = "fit",
  get_map_names = FALSE,
  get_test_name = FALSE
)

find_par_names(object, fit_name = "fit")

Arguments

Details

This function is to be used in power4test() for testing all free and user-defined model parameters, by setting it to the test_fun argument.

For models fitted by lavaan, it uses lavaan::parameterEstimates() to do the test. If bootstrapping was requested (by setting se = "boot"), then it supports bootstrap confidence intervals returned by lavaan::parameterEstimates().

It has preliminary, though limited, supported for models fitted by stats::lm() (through lmhelprs::many_lm()). Tests are conducted by ordinary least squares confidence intervals based on the t statistic, reported by stats::confint() applied to the output of stats::lm().

Value

In its normal usage, it returns the output returned by lavaan::parameterEstimates() or lmhelprs::lm_list_to_partable(), with the following modifications:

• est: The parameter estimates, even if standardized estimates are requested (not est.std).

• cilo and cihi: The lower and upper limits of the confidence interval (95% by default), respectively (not ci.lower and ci.upper).

• sig: Whether a test by confidence interval is significant (1) or not significant (0).

• test_label: A column of labels generated by lavaan::lav_partable_labels(), which are usually the labels used by coef() to label the parameters.

Find the names of parameters

To use the argument pars, the names as appeared in the function coef() must be used. For the output of lavaan, this can usually be inferred from the parameter syntax (e.g., y~x, no space). If not sure, call coef() on the output of lavaan. If a parameter is labelled, then the label should be used in par.

If not sure, the function find_par_names() can be used to find valid names.

See Also

power4test()

Examples

# Specify the model

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

# Specify the population values

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

# Simulate the data

sim_only <- power4test(nrep = 2,
                       model = mod,
                       pop_es = mod_es,
                       n = 100,
                       do_the_test = FALSE,
                       iseed = 1234)

# Do the tests in each replication

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

print(test_out,
      test_long = TRUE)

# Do the tests in each replication: Standardized solution
# Delta method SEs will be used to do the tests

test_out <- power4test(object = sim_only,
                       test_fun = test_parameters,
                       test_args = list(standardized = TRUE))

print(test_out,
      test_long = TRUE)

# Do the tests in each replication: Parameters with the selected operator

test_out <- power4test(object = sim_only,
                       test_fun = test_parameters,
                       test_args = list(op = "~"))

print(test_out,
      test_long = TRUE)


# Finding valid parameter names

find_par_names(sim_only)

Sample Size and Effect Size Determination

Description

It searches by simulation the sample size (given other factors, such as effect sizes) or effect size (given other factors, such as sample size) with power to detect an effect close to a target value.

Usage

x_from_power(
  object,
  x,
  pop_es_name = NULL,
  target_power = 0.8,
  what = c("point", "ub", "lb"),
  goal = switch(what, point = "ci_hit", ub = "close_enough", lb = "close_enough"),
  ci_level = 0.95,
  tolerance = 0.02,
  x_interval = switch(x, n = c(50, 2000), es = NULL),
  extendInt = NULL,
  progress = TRUE,
  simulation_progress = TRUE,
  max_trials = 10,
  final_nrep = 400,
  final_R = 1000,
  seed = NULL,
  x_include_interval = FALSE,
  check_es_interval = TRUE,
  power_curve_args = list(power_model = NULL, start = NULL, lower_bound = NULL,
    upper_bound = NULL, nls_control = list(), nls_args = list()),
  save_sim_all = FALSE,
  algorithm = NULL,
  control = list()
)

n_from_power(
  object,
  pop_es_name = NULL,
  target_power = 0.8,
  what = c("point", "ub", "lb"),
  goal = switch(what, point = "ci_hit", ub = "close_enough", lb = "close_enough"),
  ci_level = 0.95,
  tolerance = 0.02,
  x_interval = c(50, 2000),
  extendInt = NULL,
  progress = TRUE,
  simulation_progress = TRUE,
  max_trials = 10,
  final_nrep = 400,
  final_R = 1000,
  seed = NULL,
  x_include_interval = FALSE,
  check_es_interval = TRUE,
  power_curve_args = list(power_model = NULL, start = NULL, lower_bound = NULL,
    upper_bound = NULL, nls_control = list(), nls_args = list()),
  save_sim_all = FALSE,
  algorithm = NULL,
  control = list()
)

n_region_from_power(
  object,
  pop_es_name = NULL,
  target_power = 0.8,
  ci_level = 0.95,
  tolerance = 0.02,
  x_interval = c(50, 2000),
  extendInt = NULL,
  progress = TRUE,
  simulation_progress = TRUE,
  max_trials = 10,
  final_nrep = 400,
  final_R = 1000,
  seed = NULL,
  x_include_interval = FALSE,
  check_es_interval = TRUE,
  power_curve_args = list(power_model = NULL, start = NULL, lower_bound = NULL,
    upper_bound = NULL, nls_control = list(), nls_args = list()),
  save_sim_all = FALSE,
  algorithm = NULL,
  control = list()
)

## S3 method for class 'x_from_power'
print(x, digits = 3, ...)

## S3 method for class 'n_region_from_power'
print(x, digits = 3, ...)

Arguments

Details

This is how to use x_from_power():

• Specify the model by power4test(), with do_the_test = FALSE, and set the magnitude of the effect sizes to the minimum levels to detect.

• Add the test using power4test() using test_fun and test_args (see the help page of power4test() for details). Run it on the starting sample size or effect size.

• Call x_from_power() on the output of power4test() returned from the previous step. This function will iteratively repeat the analysis on either other sample sizes, or other values for a selected model parameter (the effect sizes), trying to achieve a goal (goal) for a value of interest (what).

If the goal is "ci_hit", the search will try to find a value (a sample size, or a population value of the selected model parameter) with a power level close enough to the target power, defined by having its confidence interval for the power including the target power.

If the goal is "close_enough", then the search will try to find a value of x with its level of power ("point"), the upper bound of the confidence interval for this level of power ("ub"), or the lower bound of the confidence interval fro this level of power ("lb") "close enough" to the target level of power, defined by having an absolute difference less than the tolerance.

If several values of x (sample size or the population value of a model parameter) have already been examined by power4test_by_n() or power4test_by_es(), the output of these two functions can also be used as object by x_from_power().

Usually, the default values of the arguments should be sufficient.

The results can be viewed using summary(), and the output has a plot method (plot.x_from_power()) to plot the relation between power and values (of x) examined.

A detailed illustration on how to use this function for sample size can be found from this page:

https://sfcheung.github.io/power4mome/articles/x_from_power_for_n.html

The function n_from_power() is just a wrapper of x_from_power(), with x set to "n".

The function n_region_from_power() is just a wrapper of x_from_power(), with x set to "n", with two passes, one with what = "ub" and one with what = "lb".

The print method only prints basic information. Call the summary method of x_from_power objects (summary.x_from_power()) and its print method for detailed results

Value

The function x_from_power() returns an x_from_power object, which is a list with the following elements:

• power4test_trials: The output of power4test_by_n() for all sample sizes examined, or of power4test_by_es() for all population values of the selected parameter examined.

• rejection_rates: The output of rejection_rates().

• x_tried: The sample sizes or population values examined.

• power_tried: The estimated rejection rates for all the values examined.

• x_final: The sample size or population value in the solution. NA if a solution not found.

• power_final: The estimated power of the value in the solution. NA if a solution not found.

• i_final: The position of the solution in power4test_trials. NA if a solution not found.

• ci_final: The confidence interval of the estimated power in the solution, formed by normal approximation. NA if a solution not found.

• ci_level: The level of confidence of ci_final.

• nrep_final: The number of replications (nrep) when estimating the power in the solution.

• power_curve: The output of power_curve() when estimating the power curve.

• target_power: The requested target power.

• power_tolerance: The allowed difference between the solution's estimated power and the target power. Determined by the number of replications and the level of confidence of the confidence intervals.

• x_estimated: The value (sample size or population value) with the target power, estimated by power_curve. This is used, when solution not found, to determine the range of the values to search when calling the function again.

• start: The time and date when the process started.

• end: The time and date when the process ended.

• time_spent: The time spent in doing the search.

• args: A named list of the arguments of x_from_power() used in the search.

• call: The call when this function is called.

The function n_region_from_power() returns a named list of two output of n_from_power(), of the class n_region_from_power. The output with what = "ub" is named "below", and the output with what = "lb" is namd "above".

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

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

Algorithms

Two algorithms are currently available, the simple (though inefficient) bisection method, and a method that makes use of the estimated crude power curve.

Unlike typical root-finding problems, the prediction of the level of power is stochastic. Moreover, the computational cost is high when Monte Carlo or bootstrap confidence intervals are used to do a test because the estimation of the power for one single value of x can sometimes take one minute or longer. Therefore, in addition to the simple bisection method, a method, named power curve method, was also specifically developed for this scenario.

Bisection Method

This method, algorithm = "bisection", basically starts with an interval that probably encloses the value of x that meets the goal, and then successively narrows this interval. The mid-point of this interval is used as the estimate. Though simple, there are cases in which it can be slow. Nevertheless, preliminary examination suggests that this method is good enough for common scenarios. Therefore, this method is the default algorithm when x is n.

Power Curve Method

This method, algorithm = "power_curve", starts with a crude power curve based on a few points. This tentative model is then used to suggest the values to examine in the next iteration. The form, not just the parameters, of the model can change across iterations, as more and more data points are available.

This method can be used only with the goal "ci_hit". This method is the default method for x = "es" with goal = "ci_hit" because the relation between the power and the population value of a parameter varies across parameters, unlike the relation between power and sample size. Therefore, taking into account the working power curve may help finding the desired value of x.

The technical internal workflow of this method implemented in x_from_power() can be found in this page: https://sfcheung.github.io/power4mome/articles/x_from_power_workflow.html.

See Also

power4test(), power4test_by_n(), and power4test_by_es().

Examples

# Specify the population model

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

# Specify the population values

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

# Generate the datasets

sim_only <- power4test(nrep = 5,
                       model = mod,
                       pop_es = mod_es,
                       n = 100,
                       do_the_test = FALSE,
                       iseed = 2345)

# Do a test

test_out <- power4test(object = sim_only,
                       test_fun = test_parameters,
                       test_args = list(pars = "m~x"))

# Determine the sample size with a power of .80 (default)

# In real analysis, to have more stable results:
# - Use a larger final_nrep (e.g., 400).

# If the default values are OK, this call is sufficient:
# power_vs_n <- x_from_power(test_out,
#                            x = "n",
#                            seed = 4567)
power_vs_n <- x_from_power(test_out,
                           x = "n",
                           progress = TRUE,
                           target_power = .80,
                           final_nrep = 5,
                           max_trials = 1,
                           seed = 1234)
summary(power_vs_n)
plot(power_vs_n)