Print the content of the
output of `many_indirect_effects()`

.

## Usage

```
# S3 method for class 'indirect_list'
print(
x,
digits = 3,
annotation = TRUE,
pvalue = FALSE,
pvalue_digits = 3,
se = FALSE,
for_each_path = FALSE,
...
)
```

## Arguments

- x
The output of

`many_indirect_effects()`

.- digits
Number of digits to display. Default is 3.

- annotation
Logical. Whether the annotation after the table of effects is to be printed. Default is

`TRUE.`

- pvalue
Logical. If

`TRUE`

, asymmetric*p*-values based on bootstrapping will be printed if available.- pvalue_digits
Number of decimal places to display for the

*p*-values. Default is 3.- se
Logical. If

`TRUE`

and confidence intervals are available, the standard errors of the estimates are also printed. 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.- for_each_path
Logical. If

`TRUE`

, each of the paths will be printed individually, using the`print`

-method of the output of`indirect_effect()`

. Default is`FALSE`

.- ...
Other arguments. If

`for_each_path`

is`TRUE`

, they will be passed to the print method of the output of`indirect_effect()`

. Ignored otherwise.

## Details

The `print`

method of the
`indirect_list`

-class object.

If bootstrapping confidence interval
was requested, this method has the
option to print a
*p*-value computed by the
method presented in Asparouhov and Muthén (2021).
Note that this *p*-value is asymmetric
bootstrap *p*-value based on the
distribution of the bootstrap estimates.
It is 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.
#>
```