Nothing
Mediation Models with Latent Variables"
In manymome: Mediation, Moderation and Moderated-Mediation After Model Fitting
Introduction
This article is a brief illustration of how indirect_effect()
from the package
manymome
(Cheung & Cheung, 2023)
can be used to estimate the indirect effects among
latent variables and form bootstrap confidence
intervals for these effects.
Data Set and Model
This is the sample dataset used for
illustration:
library(manymome)
dat <- data_sem
print(round(head(dat), 1))
#> x01 x02 x03 x04 x05 x06 x07 x08 x09 x10 x11 x12 x13 x14
#> 1 0.2 -1.1 -0.2 2.7 -0.1 2.2 1.7 0.2 -1.9 -2.1 -0.9 -0.5 -0.6 -1.5
#> 2 0.1 0.3 0.9 -0.5 -1.0 0.8 -0.3 -0.9 0.8 -1.5 -0.7 1.0 -0.8 -1.4
#> 3 -0.1 -0.1 2.4 -0.2 -0.1 -1.6 -1.9 -2.4 -0.3 -0.2 0.5 -0.7 0.2 -0.7
#> 4 -0.5 1.1 -0.7 -0.5 1.0 -0.6 -0.9 -1.5 -1.5 -0.5 -2.2 -1.6 -0.8 -0.2
#> 5 -0.8 -0.3 -0.9 -0.2 0.3 -2.1 -0.5 -1.8 -0.1 -2.7 -0.4 -1.2 0.3 -2.6
#> 6 0.9 0.3 0.3 1.0 -1.8 -0.4 0.8 -0.4 -1.1 -2.1 0.4 0.0 -0.3 2.2
This dataset has 14 variables, which
are indicators of four latent factors:
f1, f2, f3, and f4.
Suppose this is the model to be fitted:
This model can be fitted by lavaan::sem():
mod <-
"
f1 =~ x01 + x02 + x03
f2 =~ x04 + x05 + x06 + x07
f3 =~ x08 + x09 + x10
f4 =~ x11 + x12 + x13 + x14
f3 ~ f1 + f2
f4 ~ f1 + f3
"
fit_med <- sem(model = mod,
data = dat)
These are the estimates of
the paths between the latent variables:
est <- parameterEstimates(fit_med)
est[est$op == "~", ]
#> lhs op rhs est se z pvalue ci.lower ci.upper
#> 15 f3 ~ f1 0.243 0.120 2.018 0.044 0.007 0.479
#> 16 f3 ~ f2 0.326 0.102 3.186 0.001 0.125 0.526
#> 17 f4 ~ f1 0.447 0.125 3.592 0.000 0.203 0.692
#> 18 f4 ~ f3 0.402 0.090 4.445 0.000 0.225 0.579
Suppose that for the free parameters,
we would like to use ML to form the
confidence intervals. For indirect
effects, we want to use bootstrapping.
Generating Bootstrap Estimates
Although bootstrap estimates can
be generated and stored the first time
we call indirect_effect(), we illustrate
using do_boot() to generate the
bootstrap estimates to be used by
indirect_effect():
boot_out_med <- do_boot(fit_med,
R = 100,
seed = 98171,
ncores = 1)
Please see vignette("do_boot") or
the help page of do_boot() on how
to use this function. In real research,
R, the number of bootstrap samples,
should be set to 2000 or even 5000.
The argument ncores can usually be omitted
unless users want to manually control
the number of CPU cores to be used in
parallel processing.
Indirect Effects
Even though path coefficients are not labelled,
we can still use indirect_effect() to
estimate the indirect effect and form its
bootstrap confidence interval for any path
in the model. By reusing the generated bootstrap
estimates, there is no need to repeat the
resampling and estimation.
Suppose we want to estimate the indirect
effect from f1 to f4 through f3:
out_f1f3f4 <- indirect_effect(x = "f1",
y = "f4",
m = "f3",
fit = fit_med,
boot_ci = TRUE,
boot_out = boot_out_med)
out_f1f3f4
#>
#> == Indirect Effect ==
#>
#> Path: f1 -> f3 -> f4
#> Indirect Effect: 0.098
#> 95.0% Bootstrap CI: [-0.007 to 0.216]
#>
#> Computation Formula:
#> (b.f3~f1)*(b.f4~f3)
#>
#> Computation:
#> (0.24307)*(0.40186)
#>
#>
#> Percentile confidence interval formed by nonparametric bootstrapping
#> with 100 bootstrap samples.
#>
#> Coefficients of Component Paths:
#> Path Coefficient
#> f3~f1 0.243
#> f4~f3 0.402
The indirect effect is
0.098,
with 95% confidence interval
[-0.007, 0.216].
Similarly, we can estimate the indirect
effect from f2 to f4 through f3:
out_f2f3f4 <- indirect_effect(x = "f2",
y = "f4",
m = "f3",
fit = fit_med,
boot_ci = TRUE,
boot_out = boot_out_med)
out_f2f3f4
#>
#> == Indirect Effect ==
#>
#> Path: f2 -> f3 -> f4
#> Indirect Effect: 0.131
#> 95.0% Bootstrap CI: [0.049 to 0.254]
#>
#> Computation Formula:
#> (b.f3~f2)*(b.f4~f3)
#>
#> Computation:
#> (0.32561)*(0.40186)
#>
#>
#> Percentile confidence interval formed by nonparametric bootstrapping
#> with 100 bootstrap samples.
#>
#> Coefficients of Component Paths:
#> Path Coefficient
#> f3~f2 0.326
#> f4~f3 0.402
The indirect effect is
0.131,
with 95% confidence interval
[0.049, 0.254].
Standardized Indirect effects
The standardized indirect
effect from f1 to f4 through f3
can be estimated by setting
standardized_x and standardized_y to `TRUE:
std_f1f3f4 <- indirect_effect(x = "f1",
y = "f4",
m = "f3",
fit = fit_med,
boot_ci = TRUE,
boot_out = boot_out_med,
standardized_x = TRUE,
standardized_y = TRUE)
std_f1f3f4
#>
#> == Indirect Effect (Both 'f1' and 'f4' Standardized) ==
#>
#> Path: f1 -> f3 -> f4
#> Indirect Effect: 0.073
#> 95.0% Bootstrap CI: [-0.005 to 0.157]
#>
#> Computation Formula:
#> (b.f3~f1)*(b.f4~f3)*sd_f1/sd_f4
#>
#> Computation:
#> (0.24307)*(0.40186)*(0.87470)/(1.17421)
#>
#>
#> Percentile confidence interval formed by nonparametric bootstrapping
#> with 100 bootstrap samples.
#>
#> Coefficients of Component Paths:
#> Path Coefficient
#> f3~f1 0.243
#> f4~f3 0.402
#>
#> NOTE:
#> - The effects of the component paths are from the model, not
#> standardized.
The standardized indirect effect is
0.073,
with 95% confidence interval
[-0.005, 0.157].
Similarly, we can estimate the standardized indirect
effect from f2 to f4 through f3:
std_f2f3f4 <- indirect_effect(x = "f2",
y = "f4",
m = "f3",
fit = fit_med,
boot_ci = TRUE,
boot_out = boot_out_med,
standardized_x = TRUE,
standardized_y = TRUE)
std_f2f3f4
#>
#> == Indirect Effect (Both 'f2' and 'f4' Standardized) ==
#>
#> Path: f2 -> f3 -> f4
#> Indirect Effect: 0.116
#> 95.0% Bootstrap CI: [0.044 to 0.204]
#>
#> Computation Formula:
#> (b.f3~f2)*(b.f4~f3)*sd_f2/sd_f4
#>
#> Computation:
#> (0.32561)*(0.40186)*(1.03782)/(1.17421)
#>
#>
#> Percentile confidence interval formed by nonparametric bootstrapping
#> with 100 bootstrap samples.
#>
#> Coefficients of Component Paths:
#> Path Coefficient
#> f3~f2 0.326
#> f4~f3 0.402
#>
#> NOTE:
#> - The effects of the component paths are from the model, not
#> standardized.
The standardized indirect effect is
0.116,
with 95% confidence interval
[0.044, 0.204].
Note that, unlike the confidence intervals in
lavaan::standardizedSolution(), the confidence
intervals formed by indirect_effect()
are the bootstrap confidence intervals formed
based on the bootstrap estimates, rather than
intervals based on the delta method.
Adding Effects
Note that the results of indirect_effect()
can be added using +.
For example, to find the total effect of
f1 on f4, we also need to compute
the direct effect from f1 to f4. Although
it is already available in the lavaan output,
we still use indirect_effect() to compute it
so that it can be added to the indirect effect
computed above with bootstrap confidence
interval:
out_f1f4 <- indirect_effect(x = "f1",
y = "f4",
fit = fit_med,
boot_ci = TRUE,
boot_out = boot_out_med)
out_f1f4
#>
#> == Effect ==
#>
#> Path: f1 -> f4
#> Effect: 0.447
#> 95.0% Bootstrap CI: [0.203 to 0.753]
#>
#> Computation Formula:
#> (b.f4~f1)
#>
#> Computation:
#> (0.44749)
#>
#>
#> Percentile confidence interval formed by nonparametric bootstrapping
#> with 100 bootstrap samples.
We can now compute the total effect:
out_f1_total <- out_f1f3f4 + out_f1f4
out_f1_total
#>
#> == Indirect Effect ==
#>
#> Path: f1 -> f3 -> f4
#> Path: f1 -> f4
#> Function of Effects: 0.545
#> 95.0% Bootstrap CI: [0.318 to 0.858]
#>
#> Computation of the Function of Effects:
#> (f1->f3->f4)
#> +(f1->f4)
#>
#>
#> Percentile confidence interval formed by nonparametric bootstrapping
#> with 100 bootstrap samples.
The total effect of f1 on f4 is
0.545,
with 95% confidence interval
[0.318, 0.858].
Differences in Effects
Subtraction can also be conducted using -. For
example, we can compute the difference between
the indirect effect of f1 on f4 and the
direct effect of f1 on f4:
out_f1_diff <- out_f1f4 - out_f1f3f4
out_f1_diff
#>
#> == Indirect Effect ==
#>
#> Path: f1 -> f4
#> Path: f1 -> f3 -> f4
#> Function of Effects: 0.350
#> 95.0% Bootstrap CI: [-0.027 to 0.700]
#>
#> Computation of the Function of Effects:
#> (f1->f4)
#> -(f1->f3->f4)
#>
#>
#> Percentile confidence interval formed by nonparametric bootstrapping
#> with 100 bootstrap samples.
The difference in effects is
0.350,
with 95% confidence interval
[-0.027, 0.700].
Identifying All Indirect paths
If there are several indirect paths in
a model, the function all_indirect_paths()
can
be used to automatically identify all
indirect paths (a path with at least
one mediator) in a model:
all_paths <- all_indirect_paths(fit = fit_med)
all_paths
#> Call:
#> all_indirect_paths(fit = fit_med)
#> Path(s):
#> path
#> 1 f1 -> f3 -> f4
#> 2 f2 -> f3 -> f4
The output is a all_paths-class object.
It can be used in many_indirect_effects()
out_all <- many_indirect_effects(paths = all_paths,
fit = fit_med,
boot_ci = TRUE,
boot_out = boot_out_med)
The first argument, paths, is the output
of all_indirect_paths(). The other arguments
will be passed to indirect_effect().
The output is an indirect_list-class
object, which is a list of the outputs
of indirect_effects(). If printed,
a summary of the indirect effects will
be printed:
out_all
#>
#> == Indirect Effect(s) ==
#> ind CI.lo CI.hi Sig
#> f1 -> f3 -> f4 0.098 -0.007 0.216
#> f2 -> f3 -> f4 0.131 0.049 0.254 Sig
#>
#> - [CI.lo to CI.hi] are 95.0% percentile confidence intervals by
#> nonparametric bootstrapping with 100 samples.
#> - The 'ind' column shows the indirect effects.
#>
The output of many_indirect_effects()
is a named list, names being the path
name as appeared in the output.
Individual indirect
effects can be extracted using either
the indices or the path names
An example using index:
out1 <- out_all[[1]]
out1
#>
#> == Indirect Effect ==
#>
#> Path: f1 -> f3 -> f4
#> Indirect Effect: 0.098
#> 95.0% Bootstrap CI: [-0.007 to 0.216]
#>
#> Computation Formula:
#> (b.f3~f1)*(b.f4~f3)
#>
#> Computation:
#> (0.24307)*(0.40186)
#>
#>
#> Percentile confidence interval formed by nonparametric bootstrapping
#> with 100 bootstrap samples.
#>
#> Coefficients of Component Paths:
#> Path Coefficient
#> f3~f1 0.243
#> f4~f3 0.402
An example using path name (though not
recommended because the name is usually
long):
out2 <- out_all[["f2 -> f3 -> f4"]]
out2
#>
#> == Indirect Effect ==
#>
#> Path: f2 -> f3 -> f4
#> Indirect Effect: 0.131
#> 95.0% Bootstrap CI: [0.049 to 0.254]
#>
#> Computation Formula:
#> (b.f3~f2)*(b.f4~f3)
#>
#> Computation:
#> (0.32561)*(0.40186)
#>
#>
#> Percentile confidence interval formed by nonparametric bootstrapping
#> with 100 bootstrap samples.
#>
#> Coefficients of Component Paths:
#> Path Coefficient
#> f3~f2 0.326
#> f4~f3 0.402
The extracted element can be used just
like the outputs of indirect_effect()
in previous section.
Users can customize the search.
For example, if
a model has control variables, they can
be excluded in the search for indirect
paths. Users can also limit the search
to paths that start from or end at
selected variables.
See the help page of all_indirect_paths()
and many_indirect_effects() for the
arguments available.
Not demonstrated in this document,
total indirect effect can be computed
by total_indirect_effect() from the
output of many_indirect_effects().
Please refer to vignette("med_lm")
for an example and the help page of
total_indirect_effect().
Further Information
For further information on do_boot()
and indirect_effect(),
please refer to their help pages,
or vignette("manymome") and
vignette("do_boot").
Monte Carlo confidence intervals can also
be formed using the functions illustrated
above. First use do_mc() instead of
do_boot() to generate simulated sample
estimates. When calling other main
functions, use mc_ci = TRUE and set
mc_out to the output of do_mc().
Please refer to vignette("do_mc")
for an illustration.
Reference
Cheung, S. F., & Cheung, S.-H. (2023). manymome: An R
package for computing the indirect effects, conditional
effects, and conditional indirect effects, standardized or
unstandardized, and their bootstrap confidence intervals,
in many (though not all) models. Behavior Research Methods.
https://doi.org/10.3758/s13428-023-02224-z
Try the manymome package in your browser
Any scripts or data that you put into this service are public.
manymome documentation built on Oct. 4, 2024, 5:10 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Introduction
This article is a brief illustration of how indirect_effect()
from the package
manymome
(Cheung & Cheung, 2023)
can be used to estimate the indirect effects among
latent variables and form bootstrap confidence
intervals for these effects.
Data Set and Model
This is the sample dataset used for illustration:
library(manymome) dat <- data_sem print(round(head(dat), 1)) #> x01 x02 x03 x04 x05 x06 x07 x08 x09 x10 x11 x12 x13 x14 #> 1 0.2 -1.1 -0.2 2.7 -0.1 2.2 1.7 0.2 -1.9 -2.1 -0.9 -0.5 -0.6 -1.5 #> 2 0.1 0.3 0.9 -0.5 -1.0 0.8 -0.3 -0.9 0.8 -1.5 -0.7 1.0 -0.8 -1.4 #> 3 -0.1 -0.1 2.4 -0.2 -0.1 -1.6 -1.9 -2.4 -0.3 -0.2 0.5 -0.7 0.2 -0.7 #> 4 -0.5 1.1 -0.7 -0.5 1.0 -0.6 -0.9 -1.5 -1.5 -0.5 -2.2 -1.6 -0.8 -0.2 #> 5 -0.8 -0.3 -0.9 -0.2 0.3 -2.1 -0.5 -1.8 -0.1 -2.7 -0.4 -1.2 0.3 -2.6 #> 6 0.9 0.3 0.3 1.0 -1.8 -0.4 0.8 -0.4 -1.1 -2.1 0.4 0.0 -0.3 2.2
This dataset has 14 variables, which
are indicators of four latent factors:
f1, f2, f3, and f4.
Suppose this is the model to be fitted:
This model can be fitted by lavaan::sem():
mod <- " f1 =~ x01 + x02 + x03 f2 =~ x04 + x05 + x06 + x07 f3 =~ x08 + x09 + x10 f4 =~ x11 + x12 + x13 + x14 f3 ~ f1 + f2 f4 ~ f1 + f3 " fit_med <- sem(model = mod, data = dat)
These are the estimates of the paths between the latent variables:
est <- parameterEstimates(fit_med) est[est$op == "~", ] #> lhs op rhs est se z pvalue ci.lower ci.upper #> 15 f3 ~ f1 0.243 0.120 2.018 0.044 0.007 0.479 #> 16 f3 ~ f2 0.326 0.102 3.186 0.001 0.125 0.526 #> 17 f4 ~ f1 0.447 0.125 3.592 0.000 0.203 0.692 #> 18 f4 ~ f3 0.402 0.090 4.445 0.000 0.225 0.579
Suppose that for the free parameters, we would like to use ML to form the confidence intervals. For indirect effects, we want to use bootstrapping.
Generating Bootstrap Estimates
Although bootstrap estimates can
be generated and stored the first time
we call indirect_effect(), we illustrate
using do_boot() to generate the
bootstrap estimates to be used by
indirect_effect():
boot_out_med <- do_boot(fit_med, R = 100, seed = 98171, ncores = 1)
Please see vignette("do_boot") or
the help page of do_boot() on how
to use this function. In real research,
R, the number of bootstrap samples,
should be set to 2000 or even 5000.
The argument ncores can usually be omitted
unless users want to manually control
the number of CPU cores to be used in
parallel processing.
Indirect Effects
Even though path coefficients are not labelled,
we can still use indirect_effect() to
estimate the indirect effect and form its
bootstrap confidence interval for any path
in the model. By reusing the generated bootstrap
estimates, there is no need to repeat the
resampling and estimation.
Suppose we want to estimate the indirect
effect from f1 to f4 through f3:
out_f1f3f4 <- indirect_effect(x = "f1", y = "f4", m = "f3", fit = fit_med, boot_ci = TRUE, boot_out = boot_out_med) out_f1f3f4 #> #> == Indirect Effect == #> #> Path: f1 -> f3 -> f4 #> Indirect Effect: 0.098 #> 95.0% Bootstrap CI: [-0.007 to 0.216] #> #> Computation Formula: #> (b.f3~f1)*(b.f4~f3) #> #> Computation: #> (0.24307)*(0.40186) #> #> #> Percentile confidence interval formed by nonparametric bootstrapping #> with 100 bootstrap samples. #> #> Coefficients of Component Paths: #> Path Coefficient #> f3~f1 0.243 #> f4~f3 0.402
The indirect effect is 0.098, with 95% confidence interval [-0.007, 0.216].
Similarly, we can estimate the indirect
effect from f2 to f4 through f3:
out_f2f3f4 <- indirect_effect(x = "f2", y = "f4", m = "f3", fit = fit_med, boot_ci = TRUE, boot_out = boot_out_med) out_f2f3f4 #> #> == Indirect Effect == #> #> Path: f2 -> f3 -> f4 #> Indirect Effect: 0.131 #> 95.0% Bootstrap CI: [0.049 to 0.254] #> #> Computation Formula: #> (b.f3~f2)*(b.f4~f3) #> #> Computation: #> (0.32561)*(0.40186) #> #> #> Percentile confidence interval formed by nonparametric bootstrapping #> with 100 bootstrap samples. #> #> Coefficients of Component Paths: #> Path Coefficient #> f3~f2 0.326 #> f4~f3 0.402
The indirect effect is 0.131, with 95% confidence interval [0.049, 0.254].
Standardized Indirect effects
The standardized indirect
effect from f1 to f4 through f3
can be estimated by setting
standardized_x and standardized_y to `TRUE:
std_f1f3f4 <- indirect_effect(x = "f1", y = "f4", m = "f3", fit = fit_med, boot_ci = TRUE, boot_out = boot_out_med, standardized_x = TRUE, standardized_y = TRUE) std_f1f3f4 #> #> == Indirect Effect (Both 'f1' and 'f4' Standardized) == #> #> Path: f1 -> f3 -> f4 #> Indirect Effect: 0.073 #> 95.0% Bootstrap CI: [-0.005 to 0.157] #> #> Computation Formula: #> (b.f3~f1)*(b.f4~f3)*sd_f1/sd_f4 #> #> Computation: #> (0.24307)*(0.40186)*(0.87470)/(1.17421) #> #> #> Percentile confidence interval formed by nonparametric bootstrapping #> with 100 bootstrap samples. #> #> Coefficients of Component Paths: #> Path Coefficient #> f3~f1 0.243 #> f4~f3 0.402 #> #> NOTE: #> - The effects of the component paths are from the model, not #> standardized.
The standardized indirect effect is 0.073, with 95% confidence interval [-0.005, 0.157].
Similarly, we can estimate the standardized indirect
effect from f2 to f4 through f3:
std_f2f3f4 <- indirect_effect(x = "f2", y = "f4", m = "f3", fit = fit_med, boot_ci = TRUE, boot_out = boot_out_med, standardized_x = TRUE, standardized_y = TRUE) std_f2f3f4 #> #> == Indirect Effect (Both 'f2' and 'f4' Standardized) == #> #> Path: f2 -> f3 -> f4 #> Indirect Effect: 0.116 #> 95.0% Bootstrap CI: [0.044 to 0.204] #> #> Computation Formula: #> (b.f3~f2)*(b.f4~f3)*sd_f2/sd_f4 #> #> Computation: #> (0.32561)*(0.40186)*(1.03782)/(1.17421) #> #> #> Percentile confidence interval formed by nonparametric bootstrapping #> with 100 bootstrap samples. #> #> Coefficients of Component Paths: #> Path Coefficient #> f3~f2 0.326 #> f4~f3 0.402 #> #> NOTE: #> - The effects of the component paths are from the model, not #> standardized.
The standardized indirect effect is 0.116, with 95% confidence interval [0.044, 0.204].
Note that, unlike the confidence intervals in
lavaan::standardizedSolution(), the confidence
intervals formed by indirect_effect()
are the bootstrap confidence intervals formed
based on the bootstrap estimates, rather than
intervals based on the delta method.
Adding Effects
Note that the results of indirect_effect()
can be added using +.
For example, to find the total effect of
f1 on f4, we also need to compute
the direct effect from f1 to f4. Although
it is already available in the lavaan output,
we still use indirect_effect() to compute it
so that it can be added to the indirect effect
computed above with bootstrap confidence
interval:
out_f1f4 <- indirect_effect(x = "f1", y = "f4", fit = fit_med, boot_ci = TRUE, boot_out = boot_out_med) out_f1f4 #> #> == Effect == #> #> Path: f1 -> f4 #> Effect: 0.447 #> 95.0% Bootstrap CI: [0.203 to 0.753] #> #> Computation Formula: #> (b.f4~f1) #> #> Computation: #> (0.44749) #> #> #> Percentile confidence interval formed by nonparametric bootstrapping #> with 100 bootstrap samples.
We can now compute the total effect:
out_f1_total <- out_f1f3f4 + out_f1f4 out_f1_total #> #> == Indirect Effect == #> #> Path: f1 -> f3 -> f4 #> Path: f1 -> f4 #> Function of Effects: 0.545 #> 95.0% Bootstrap CI: [0.318 to 0.858] #> #> Computation of the Function of Effects: #> (f1->f3->f4) #> +(f1->f4) #> #> #> Percentile confidence interval formed by nonparametric bootstrapping #> with 100 bootstrap samples.
The total effect of f1 on f4 is
0.545,
with 95% confidence interval
[0.318, 0.858].
Differences in Effects
Subtraction can also be conducted using -. For
example, we can compute the difference between
the indirect effect of f1 on f4 and the
direct effect of f1 on f4:
out_f1_diff <- out_f1f4 - out_f1f3f4 out_f1_diff #> #> == Indirect Effect == #> #> Path: f1 -> f4 #> Path: f1 -> f3 -> f4 #> Function of Effects: 0.350 #> 95.0% Bootstrap CI: [-0.027 to 0.700] #> #> Computation of the Function of Effects: #> (f1->f4) #> -(f1->f3->f4) #> #> #> Percentile confidence interval formed by nonparametric bootstrapping #> with 100 bootstrap samples.
The difference in effects is 0.350, with 95% confidence interval [-0.027, 0.700].
Identifying All Indirect paths
If there are several indirect paths in
a model, the function all_indirect_paths()
can
be used to automatically identify all
indirect paths (a path with at least
one mediator) in a model:
all_paths <- all_indirect_paths(fit = fit_med) all_paths #> Call: #> all_indirect_paths(fit = fit_med) #> Path(s): #> path #> 1 f1 -> f3 -> f4 #> 2 f2 -> f3 -> f4
The output is a all_paths-class object.
It can be used in many_indirect_effects()
out_all <- many_indirect_effects(paths = all_paths, fit = fit_med, boot_ci = TRUE, boot_out = boot_out_med)
The first argument, paths, is the output
of all_indirect_paths(). The other arguments
will be passed to indirect_effect().
The output is an indirect_list-class
object, which is a list of the outputs
of indirect_effects(). If printed,
a summary of the indirect effects will
be printed:
out_all #> #> == Indirect Effect(s) == #> ind CI.lo CI.hi Sig #> f1 -> f3 -> f4 0.098 -0.007 0.216 #> f2 -> f3 -> f4 0.131 0.049 0.254 Sig #> #> - [CI.lo to CI.hi] are 95.0% percentile confidence intervals by #> nonparametric bootstrapping with 100 samples. #> - The 'ind' column shows the indirect effects. #>
The output of many_indirect_effects()
is a named list, names being the path
name as appeared in the output.
Individual indirect
effects can be extracted using either
the indices or the path names
An example using index:
out1 <- out_all[[1]] out1 #> #> == Indirect Effect == #> #> Path: f1 -> f3 -> f4 #> Indirect Effect: 0.098 #> 95.0% Bootstrap CI: [-0.007 to 0.216] #> #> Computation Formula: #> (b.f3~f1)*(b.f4~f3) #> #> Computation: #> (0.24307)*(0.40186) #> #> #> Percentile confidence interval formed by nonparametric bootstrapping #> with 100 bootstrap samples. #> #> Coefficients of Component Paths: #> Path Coefficient #> f3~f1 0.243 #> f4~f3 0.402
An example using path name (though not recommended because the name is usually long):
out2 <- out_all[["f2 -> f3 -> f4"]] out2 #> #> == Indirect Effect == #> #> Path: f2 -> f3 -> f4 #> Indirect Effect: 0.131 #> 95.0% Bootstrap CI: [0.049 to 0.254] #> #> Computation Formula: #> (b.f3~f2)*(b.f4~f3) #> #> Computation: #> (0.32561)*(0.40186) #> #> #> Percentile confidence interval formed by nonparametric bootstrapping #> with 100 bootstrap samples. #> #> Coefficients of Component Paths: #> Path Coefficient #> f3~f2 0.326 #> f4~f3 0.402
The extracted element can be used just
like the outputs of indirect_effect()
in previous section.
Users can customize the search.
For example, if
a model has control variables, they can
be excluded in the search for indirect
paths. Users can also limit the search
to paths that start from or end at
selected variables.
See the help page of all_indirect_paths()
and many_indirect_effects() for the
arguments available.
Not demonstrated in this document,
total indirect effect can be computed
by total_indirect_effect() from the
output of many_indirect_effects().
Please refer to vignette("med_lm")
for an example and the help page of
total_indirect_effect().
Further Information
For further information on do_boot()
and indirect_effect(),
please refer to their help pages,
or vignette("manymome") and
vignette("do_boot").
Monte Carlo confidence intervals can also
be formed using the functions illustrated
above. First use do_mc() instead of
do_boot() to generate simulated sample
estimates. When calling other main
functions, use mc_ci = TRUE and set
mc_out to the output of do_mc().
Please refer to vignette("do_mc")
for an illustration.
Reference
Cheung, S. F., & Cheung, S.-H. (2023). manymome: An R package for computing the indirect effects, conditional effects, and conditional indirect effects, standardized or unstandardized, and their bootstrap confidence intervals, in many (though not all) models. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02224-z
Try the manymome package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.