# Coefficient Table of an 'indirect_list' Class Object

Source:`R/print_indirect_list.R`

`indirect_effects_from_list.Rd`

Create a coefficient table
for the point estimates and
confidence intervals (if available)
in the
output of `many_indirect_effects()`

.

## Arguments

- object
The output of

`indirect_effect()`

or`cond_indirect()`

.- add_sig
Whether a column of significance test results will be added. Default is

`TRUE`

.- pvalue
Logical. If

`TRUE`

, asymmetric*p*-values based on bootstrapping will be added available. Default is`FALSE`

.- se
Logical. If

`TRUE`

and confidence intervals are available, the standard errors of the estimates are also added. They are simply the standard deviations of the bootstrap estimates or Monte Carlo simulated values, depending on the method used to form the confidence intervals.

## Value

A data frame with the
indirect effect estimates and
confidence intervals (if available).
It also has A string column, `"Sig"`

,
for #' significant test results
if `add_sig`

is `TRUE`

and
confidence intervals are available.

## Details

If bootstrapping confidence interval
was requested, this method has the
option to add
*p*-values computed by the
method presented in Asparouhov and Muthén (2021).
Note that these *p*-values is asymmetric
bootstrap *p*-values based on the
distribution of the bootstrap estimates.
They are not computed based on the
distribution under the null hypothesis.

For a *p*-value of *a*, it means that
a 100(1 - *a*)% bootstrapping confidence
interval
will have one of its limits equal to
0. A confidence interval
with a higher confidence level will
include zero, while a confidence
interval with a lower confidence level
will exclude zero.

## References

Asparouhov, A., & Muthén, B. (2021). Bootstrap p-value computation. Retrieved from https://www.statmodel.com/download/FAQ-Bootstrap%20-%20Pvalue.pdf

## Examples

```
library(lavaan)
data(data_serial_parallel)
mod <-
"
m11 ~ x + c1 + c2
m12 ~ m11 + x + c1 + c2
m2 ~ x + c1 + c2
y ~ m12 + m2 + m11 + x + c1 + c2
"
fit <- sem(mod, data_serial_parallel,
fixed.x = FALSE)
# All indirect paths from x to y
paths <- all_indirect_paths(fit,
x = "x",
y = "y")
paths
#> Call:
#> all_indirect_paths(fit = fit, x = "x", y = "y")
#> Path(s):
#> path
#> 1 x -> m11 -> m12 -> y
#> 2 x -> m11 -> y
#> 3 x -> m12 -> y
#> 4 x -> m2 -> y
# Indirect effect estimates
out <- many_indirect_effects(paths,
fit = fit)
out
#>
#> == Indirect Effect(s) ==
#> ind
#> x -> m11 -> m12 -> y 0.193
#> x -> m11 -> y 0.163
#> x -> m12 -> y 0.059
#> x -> m2 -> y 0.364
#>
#> - The 'ind' column shows the indirect effects.
#>
# Create a data frame of the indirect effect estimates
out_df <- indirect_effects_from_list(out)
out_df
#> ind
#> x -> m11 -> m12 -> y 0.19321379
#> x -> m11 -> y 0.16261213
#> x -> m12 -> y 0.05946653
#> x -> m2 -> y 0.36440188
```