Estimate the Power of a Test

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

object: Optional. If set to a power4test object, it will be updated using the value(s) in n, pop_es, and/or nrep if they changed, or a new test will be conducted and added to objet. See the help page for details. Default is NULL.
nrep: The number of replications to generate the simulated datasets. Default is NULL. Must be set when called to create a power4test object.
ptable: The output of ptable_pop(), which is a ptable_pop object, representing the population model. If NULL, the default, ptable_pop() will be called to generate the ptable_pop object using model and pop_es.
model: The lavaan model syntax of the population model, to be used by ptable_pop(). See 'Details' of on how to specify the model. Ignored if ptable is specified.
pop_es: The character vector or multiline string to specify population effect sizes (population values of parameters). See the help page on how to specify this argument. Ignored if ptable is specified.
standardized: Logical. If TRUE, the default, variances and error variances are scaled to ensure the population variances of the endogenous variables are close to one, and hence the effect sizes (population values) are standardized effect sizes if the variances of the continuous exogenous variables are also equal to one.
n: The sample size for each dataset. Default is 100.
number_of_indicators: A named vector to specify the number of indicators for each factors. See the help page on how to set this argument. Default is NULL and all variables in the model syntax are observed variables. See the help page on how to use this argument.
reliability: A named vector (for a single-group model) or a named list of named vectors (for a multigroup model) to set the reliability coefficient of each set of indicators. Default is NULL. See the help page on how to use this argument.
x_fun: The function(s) used to generate the exogenous variables or error terms. If not supplied, or set to list(), the default, the variables are generated from a multivariate normal distribution. See the help page on how to use this argument.
e_fun: The function(s) used to generate the error terms of indicators, if any. If not supplied, or set to list(), the default, the error terms of indicators are generated from a multivariate normal distribution. Specify in the same way as x_fun. Refer to the help page on x_fun on how to use this argument.
process_data: If not NULL, it must be a named list with these elements: fun (required), the function to further processing the simulated data, such as generating missing data using functions such as mice::ampute(); args (optional), a named list of arguments to be passed to fun, except the one for the source data; sim_data_name (required) the name of the argument to receive the simulated data (e.g., data for mice::ampute()); processed_data_name (optional), the name of the data frame after being processed by fun, such as the data frame with missing data in the output of fun (e.g., "amp" for mice::ampute()), if omitted, the output of fun should be the data frame with missing data.
fit_model_args: A list of the arguments to be passed to fit_model() when fitting the model. Should be a named list with names being the names of the arguments.
R: The number of replications to generate the Monte Carlo or bootstrapping estimates for each fit output. No Monte Carlo nor bootstrapping estimates will be generated if R is set to NULL.
ci_type: The type of simulation-based confidence intervals to use. Can be either "mc" for Monte Carlo method (the default) or "boot" for nonparametric bootstrapping method. Relevant for test functions that make use of estimates generate by gen_boot() or gen_mc(), such as test_indirect_effect().
gen_mc_args: A list of arguments to be passed to manymome::do_mc() when generating the Monte Carlo estimates. Should be a named argument with names being the names of the arguments. Used only if ci_type is "mc".
gen_boot_args: A list of arguments to be passed to manymome::do_boot() when generating the bootstrap estimates. Should be a named argument with names being the names of the arguments. Used only if ci_type is `"boot".
test_fun: A function to do the test. See 'Details' for the requirement of this function. There are some built-in test functions in power4mome, described in 'Details'.
test_args: A list of arguments to be passed to the test_fun function. Default is list().
map_names: A named character vector specifying how the content of the element extra in each replication of sim_all map to the argument of test_fun. Default is c(fit = "fit"), indicating that the element fit in the element extra is set to the argument fit of test_fun. That is, for the first replication, fit = sim_out[[1]]$extra$fit when calling test_fun.
results_fun: The function to be used to extract the test results. See Details for the requirements of this function. Default is NULL, assuming that the output of test_fun can be used directly.
results_args: A list of arguments to be passed to the results_fun function. Default is list().
test_name: String. The name of the test. Default is NULL, and the name will be created from test_fun. Note that if sim_out is a power4test object and already has a test of this name stored, it will be replaced by the new results.
test_note: String. An optional note for the test, stored in the attribute test_note of the output of do_test(). Default is NULL.
do_the_test: If TRUE, do_test() will be called to do the test specified by test_fun on the fit output of each dataset.
sim_all: If set to either a sim_out object (the output of sim_out() or a power4test object (the output of power4test()), the stored datasets and fit outputs will be used for doing the test. Setting object to the output of power4test() is now the preferred method, but this argument is kept for backward compatibility.
iseed: The seed for the random number generator. Default is NULL and the seed is not changed. This seed will be set only once, when calling sim_data().
parallel: If TRUE, parallel processing will be used when calling other functions, if appropriate. Default is FALSE.
progress: If TRUE, the progress of each step will be displayed. Default is TRUE.
ncores: The number of CPU cores to use if parallel processing is used.
es1: Set the values for each label of the effect size (population value) for correlations and regression paths. Used only if pop_es is a named vector or a multiline string. See the help page on how to specify this argument.
es2: Set the values for each label of the effect size (population value) for product term. Used only if pop_es is a named vector or a multiline string. See the help page on how to specify this argument.
es_ind: The names of labels denoting the effect size of an indirect effect. They will be used to determine the population values of the component paths along an indirect path.
n_std: The sample size used to determine the error variances by simulation when std_force_monte_carlo is TRUE.
std_force_monte_carlo: Logical. If FALSE, the default, standardization is done analytically if the model has no product terms, and by simulation if the model has product terms. That is, error variances required to ensure implied variances equal to one are determined by simulation. If TRUE, simulation will be used whether the model has product terms or not. Always fall back to simulation if analytical standardization failed.
x: The object to be printed.
what: A string vector of what to print, "data" for simulated data and "test" for stored test(s). Default is c("data", "test").
digits: The numbers of digits displayed after the decimal.
digits_descriptive: The number of digits displayed after the decimal for the descriptive statistics table.
data_long: If TRUE, detailed results will be printed when printing the simulated data.
test_long: If TRUE, detailed results will be printed when printing test(s).
fit_to_all_args: A named list of arguments to be passed to lavaan::sem() when the model is fitted to a sample combined from
...: Optional arguments to be passed to other print methods

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.

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).

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:

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.

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:

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)
#> Simulate the data:
#> Fit the model(s):
#> Do the test: test_parameters: CIs (pars: m~x) 

print(out,
      test_long = TRUE)
#> 
#> ====================== Model Information ======================
#> 
#> == Model on Factors/Variables ==
#> 
#> m ~ x
#> y ~ m + x
#> 
#> == Model on Variables/Indicators ==
#> 
#> m ~ x
#> y ~ m + x
#> 
#> ====== Population Values ======
#> 
#> Regressions:
#>                    Population
#>   m ~                        
#>     x                 0.300  
#>   y ~                        
#>     m                 0.500  
#>     x                 0.000  
#> 
#> Variances:
#>                    Population
#>    .m                 0.910  
#>    .y                 0.750  
#>     x                 1.000  
#> 
#> (Computing indirect effects for 2 paths ...)
#> 
#> == Population Conditional/Indirect Effect(s) ==
#> 
#> == Indirect Effect(s) ==
#> 
#>               ind
#> x -> m -> y 0.150
#> x -> y      0.000
#> 
#>  - The 'ind' column shows the indirect effect(s).
#>  
#> ======================= Data Information =======================
#> 
#> Number of Replications:  10 
#> Sample Sizes:  100 
#> 
#> Call print with 'data_long = TRUE' for further information.
#> 
#> ==================== Extra Element(s) Found ====================
#> 
#> - fit
#> 
#> === Element(s) of the First Dataset ===
#> 
#> ============ <fit> ============
#> 
#> lavaan 0.6-19 ended normally after 1 iteration
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                         5
#> 
#>   Number of observations                           100
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                                 0.000
#>   Degrees of freedom                                 0
#> 
#> ========== <test_parameters: CIs (pars: m~x)> ==========
#> 
#> Mean(s) across replication:
#>   test_label lhs op rhs   est    se     z pvalue  cilo  cihi   sig
#> 1        m~x   m  ~   x 0.320 0.098 3.234  0.092 0.128 0.512 0.800
#> 
#> - The column 'sig' shows the rejection rates.
#> - If the null hypothesis is false, the rate is the power.
#> - Number of valid replications for rejection rate(s): 10 
#> - Proportion of valid replications for rejection rate(s): 1.000 

# Change the sample size

out1 <- power4test(out,
                   n = 200,
                   iseed = 1234,
                   parallel = FALSE,
                   progress = TRUE)
#> Re-simulate the data:
#> Fit the model(s):
#> Update the test(s):
#> Update test_parameters: CIs (pars: m~x) :

print(out1,
      test_long = TRUE)
#> 
#> ====================== Model Information ======================
#> 
#> == Model on Factors/Variables ==
#> 
#> m ~ x
#> y ~ m + x
#> 
#> == Model on Variables/Indicators ==
#> 
#> m ~ x
#> y ~ m + x
#> 
#> ====== Population Values ======
#> 
#> Regressions:
#>                    Population
#>   m ~                        
#>     x                 0.300  
#>   y ~                        
#>     m                 0.500  
#>     x                 0.000  
#> 
#> Variances:
#>                    Population
#>    .m                 0.910  
#>    .y                 0.750  
#>     x                 1.000  
#> 
#> (Computing indirect effects for 2 paths ...)
#> 
#> == Population Conditional/Indirect Effect(s) ==
#> 
#> == Indirect Effect(s) ==
#> 
#>               ind
#> x -> m -> y 0.150
#> x -> y      0.000
#> 
#>  - The 'ind' column shows the indirect effect(s).
#>  
#> ======================= Data Information =======================
#> 
#> Number of Replications:  10 
#> Sample Sizes:  200 
#> 
#> Call print with 'data_long = TRUE' for further information.
#> 
#> ==================== Extra Element(s) Found ====================
#> 
#> - fit
#> 
#> === Element(s) of the First Dataset ===
#> 
#> ============ <fit> ============
#> 
#> lavaan 0.6-19 ended normally after 1 iteration
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                         5
#> 
#>   Number of observations                           200
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                                 0.000
#>   Degrees of freedom                                 0
#> 
#> ========== <test_parameters: CIs (pars: m~x)> ==========
#> 
#> Mean(s) across replication:
#>   test_label lhs op rhs   est    se     z pvalue  cilo  cihi   sig
#> 1        m~x   m  ~   x 0.305 0.067 4.571  0.000 0.174 0.436 1.000
#> 
#> - The column 'sig' shows the rejection rates.
#> - If the null hypothesis is false, the rate is the power.
#> - Number of valid replications for rejection rate(s): 10 
#> - Proportion of valid replications for rejection rate(s): 1.000 

# Add one more test

out2 <- power4test(out,
                   test_fun = test_parameters,
                   test_args = list(pars = "y~x"),
                   parallel = FALSE,
                   progress = TRUE)
#> Do the test: test_parameters: CIs (pars: y~x) 

print(out2,
      test_long = TRUE)
#> 
#> ====================== Model Information ======================
#> 
#> == Model on Factors/Variables ==
#> 
#> m ~ x
#> y ~ m + x
#> 
#> == Model on Variables/Indicators ==
#> 
#> m ~ x
#> y ~ m + x
#> 
#> ====== Population Values ======
#> 
#> Regressions:
#>                    Population
#>   m ~                        
#>     x                 0.300  
#>   y ~                        
#>     m                 0.500  
#>     x                 0.000  
#> 
#> Variances:
#>                    Population
#>    .m                 0.910  
#>    .y                 0.750  
#>     x                 1.000  
#> 
#> (Computing indirect effects for 2 paths ...)
#> 
#> == Population Conditional/Indirect Effect(s) ==
#> 
#> == Indirect Effect(s) ==
#> 
#>               ind
#> x -> m -> y 0.150
#> x -> y      0.000
#> 
#>  - The 'ind' column shows the indirect effect(s).
#>  
#> ======================= Data Information =======================
#> 
#> Number of Replications:  10 
#> Sample Sizes:  100 
#> 
#> Call print with 'data_long = TRUE' for further information.
#> 
#> ==================== Extra Element(s) Found ====================
#> 
#> - fit
#> 
#> === Element(s) of the First Dataset ===
#> 
#> ============ <fit> ============
#> 
#> lavaan 0.6-19 ended normally after 1 iteration
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                         5
#> 
#>   Number of observations                           100
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                                 0.000
#>   Degrees of freedom                                 0
#> 
#> ========== <test_parameters: CIs (pars: m~x)> ==========
#> 
#> Mean(s) across replication:
#>   test_label lhs op rhs   est    se     z pvalue  cilo  cihi   sig
#> 1        m~x   m  ~   x 0.320 0.098 3.234  0.092 0.128 0.512 0.800
#> 
#> - The column 'sig' shows the rejection rates.
#> - If the null hypothesis is false, the rate is the power.
#> - Number of valid replications for rejection rate(s): 10 
#> - Proportion of valid replications for rejection rate(s): 1.000 
#> 
#> ========== <test_parameters: CIs (pars: y~x)> ==========
#> 
#> Mean(s) across replication:
#>   test_label lhs op rhs    est    se      z pvalue   cilo  cihi   sig
#> 1        y~x   y  ~   x -0.069 0.093 -0.729  0.389 -0.252 0.113 0.000
#> 
#> - The column 'sig' shows the rejection rates.
#> - If the null hypothesis is false, the rate is the power.
#> - Number of valid replications for rejection rate(s): 10 
#> - Proportion of valid replications for rejection rate(s): 1.000

Usage

Arguments

Value

Details

Workflow

Updating a Condition

Adding Another Test

Model Fitting Arguments ('fit_model_args')

Specify the Population Model by 'model'

Single-Group Model

Multigroup Model

Specify 'pop_es' Using Named Vectors

Single-Group Model

Indirect Effect

Multigroup Model

Specify 'pop_es' Using a Multiline String

Single-Group Model

Multigroup Model

Which Approach To Use

Technical Details

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

Set 'number_of_indicators' and 'reliability'

Single-Group Model

Multigroup Models

Equal Numbers of Indicators and/or Reliability Coefficients

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

Major Test-Related Arguments

The test function (test_fun)

The results function (results_fun)

Built-in test functions

The argument map_names

Examples