test_k_indirect_effects: 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
)

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").

Arguments

fit: The fit object, to be passed to manymome::indirect_effect().
x: The name of the x-variable, the predictor.
m: Must be a list of character vectors. Each character vector stores the name(s) of mediator(s) along a path. The path moves from the first mediator in the vector to the last mediator in the vector. If NULL, the stored paths will be used, which are all the indirect paths in the model between x and y, by default.
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::many_indirect_effects().
omnibus: If "no", the default, then the test results for all paths are stored. If "all_sig", then only one row of test is stored, and the test is declared significant if all paths are significant. If "at_least_one_sig", then only one row of test is stored, and the test is declared significant if at least one of the paths is significant. If "at_least_k_sig", then only one row of test is stored, and the test is declared significant if at least k of the paths is significant, k determined by the argument at_least_k.
at_least_k: The minimum number of paths required to be significant for the omnibus test to be considered significant. Used when omnibus is "at_least_k_sig".
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.

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().

Examples

Run this code


# 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)