Test an Indirect Effect

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

fit: The fit object, to be passed to manymome::indirect_effect().
x: The name of the x-variable, the predictor.
m: A character vector of the name(s) of mediator(s). The path moves from the first mediator in the vector to the last mediator in the vector. Can be NULL and the path is a direct path without mediator.
y: The name of the y-variable, the outcome variable.
mc_ci: Logical. If TRUE, the default, Monte Carlo confidence intervals will be formed. This argument and boot_ci cannot be both TRUE.
mc_out: The pre-generated Monte Carlo estimates generated by manymome::do_mc, stored in a power4test object. Users should not set this argument and should let power4test() to set it automatically.
boot_ci: Logical. If TRUE, the default, nonparametric bootstrap confidence intervals will be formed. This argument and mc_ci cannot be both TRUE.
boot_out: The pre-generated bootstrap estimates generated by manymome::do_boot, stored in a power4test object. Users should not set this argument and should let power4test() to set it automatically.
check_post_check: Logical. If TRUE, the default, and the model is fitted by lavaan, the test will be conducted only if the model passes the post.check conducted by lavaan::lavInspect() (with what = "post.check").
...: Additional arguments to be passed to manymome::indirect_effect().
fit_name: The name of the model fit object to be extracted. Default is "fit". Used only when more than one model is fitted in each replication. This should be the name of the model on which the test is to be conducted.
get_map_names: Logical. Used by power4test() to determine how to extract stored information and assign them to this function. Users should not use this argument.
get_test_name: Logical. Used by power4test() to get the default name of this test. Users should not use this argument.

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).

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().

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)
#> Simulate the data:
#> Fit the model(s):
#> Generate Monte Carlo estimates:

# 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))
#> Do the test: test_indirect: x->m->y 
print(test_ind,
      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:  5 
#> Sample Sizes:  100 
#> 
#> Call print with 'data_long = TRUE' for further information.
#> 
#> ==================== Extra Element(s) Found ====================
#> 
#> - fit
#> - mc_out
#> 
#> === 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
#> 
#> =========== <mc_out> ===========
#> 
#> 
#> == A 'mc_out' class object ==
#> 
#> Number of Monte Carlo replications: 100 
#> 
#> 
#> =============== <test_indirect: x->m->y> ===============
#> 
#> Mean(s) across replication:
#>    est  cilo  cihi   sig
#>  0.163 0.051 0.296 1.000
#> 
#> - The value 'sig' is the rejection rate.
#> - If the null hypothesis is false, this is the power.
#> - Number of valid replications for rejection rate: 5 
#> - Proportion of valid replications for rejection rate: 1.000

Usage

Arguments

Value

Details

See also

Examples