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
- 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 betweenxandy, 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 andboot_cicannot be bothTRUE.- mc_out
The pre-generated Monte Carlo estimates generated by manymome::do_mc, stored in a
power4testobject. Users should not set this argument and should letpower4test()to set it automatically.- boot_ci
Logical. If
TRUE, the default, nonparametric bootstrap confidence intervals will be formed. This argument andmc_cicannot be bothTRUE.- boot_out
The pre-generated bootstrap estimates generated by manymome::do_boot, stored in a
power4testobject. Users should not set this argument and should letpower4test()to set it automatically.- check_post_check
Logical. If
TRUE, the default, and the model is fitted bylavaan, the test will be conducted only if the model passes thepost.checkconducted bylavaan::lavInspect()(withwhat = "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 leastkof the paths is significant,kdetermined by the argumentat_least_k.- at_least_k
The minimum number of paths required to be significant for the omnibus test to be considered significant. Used when
omnibusis"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.
Value
In its normal usage, it returns a data frame with the following columns:
est: The estimated indirect effect for each path.ciloandcihi: The lower and upper limits of the confidence interval (95% by default), respectively, for each indirect effectsig: 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").
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
# 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)
#> 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_k_indirect_effects,
test_args = list(x = "x",
y = "y",
mc_ci = TRUE))
#> Do the test: test_indirects: x-...->y
print(test_ind,
test_long = TRUE)
#>
#> ====================== Model Information ======================
#>
#> == Model on Factors/Variables ==
#>
#> m1 ~ x
#> m2 ~ x
#> y ~ m1 + m2 + x
#>
#> == Model on Variables/Indicators ==
#>
#> m1 ~ x
#> m2 ~ x
#> y ~ m1 + m2 + x
#>
#> ====== Population Values ======
#>
#> Regressions:
#> Population
#> m1 ~
#> x 0.300
#> m2 ~
#> x 0.500
#> y ~
#> m1 0.100
#> m2 0.100
#> x 0.000
#>
#> Variances:
#> Population
#> .m1 0.910
#> .m2 0.750
#> .y 0.977
#> x 1.000
#>
#> (Computing indirect effects for 3 paths ...)
#>
#> == Population Conditional/Indirect Effect(s) ==
#>
#> == Indirect Effect(s) ==
#>
#> ind
#> x -> m1 -> y 0.030
#> x -> m2 -> y 0.050
#> 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 0.6-20 ended normally after 1 iteration
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 8
#>
#> Number of observations 100
#>
#> Model Test User Model:
#>
#> Test statistic 0.219
#> Degrees of freedom 1
#> P-value (Chi-square) 0.640
#>
#> =========== <mc_out> ===========
#>
#>
#> == A 'mc_out' class object ==
#>
#> Number of Monte Carlo replications: 100
#>
#>
#> ============== <test_indirects: x-...->y> ==============
#>
#> Mean(s) across replication:
#> test_label est cilo cihi sig
#> 1 x -> m1 -> y 0.026 -0.042 0.103 0.000
#> 2 x -> m2 -> y 0.034 -0.081 0.155 0.200
#>
#> - 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): 5
#> - Proportion of valid replications for rejection rate(s): 1.000
# 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"))
#> Do the test: test_indirects: x-...->y
print(test_ind_all_sig,
test_long = TRUE)
#>
#> ====================== Model Information ======================
#>
#> == Model on Factors/Variables ==
#>
#> m1 ~ x
#> m2 ~ x
#> y ~ m1 + m2 + x
#>
#> == Model on Variables/Indicators ==
#>
#> m1 ~ x
#> m2 ~ x
#> y ~ m1 + m2 + x
#>
#> ====== Population Values ======
#>
#> Regressions:
#> Population
#> m1 ~
#> x 0.300
#> m2 ~
#> x 0.500
#> y ~
#> m1 0.100
#> m2 0.100
#> x 0.000
#>
#> Variances:
#> Population
#> .m1 0.910
#> .m2 0.750
#> .y 0.977
#> x 1.000
#>
#> (Computing indirect effects for 3 paths ...)
#>
#> == Population Conditional/Indirect Effect(s) ==
#>
#> == Indirect Effect(s) ==
#>
#> ind
#> x -> m1 -> y 0.030
#> x -> m2 -> y 0.050
#> 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 0.6-20 ended normally after 1 iteration
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 8
#>
#> Number of observations 100
#>
#> Model Test User Model:
#>
#> Test statistic 0.219
#> Degrees of freedom 1
#> P-value (Chi-square) 0.640
#>
#> =========== <mc_out> ===========
#>
#>
#> == A 'mc_out' class object ==
#>
#> Number of Monte Carlo replications: 100
#>
#>
#> ============== <test_indirects: x-...->y> ==============
#>
#> Mean(s) across replication:
#> test_label est cilo cihi sig
#> 1 x-...->y (All sig) NaN NaN NaN 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): 5
#> - Proportion of valid replications for rejection rate(s): 1.000