# Print a 'cond_indirect_effects' Class Object

Source:`R/print_cond_indirect_effect.R`

`print.cond_indirect_effects.Rd`

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

## Usage

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

## Arguments

- x
The output of

`cond_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. Default is`FALSE.`

- 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.- ...
Other arguments. Not used.

## Details

The `print`

method of the
`cond_indirect_effects`

-class object.

If bootstrapping confidence intervals
were requested, this method has the
option to print
*p*-values computed by the
method presented in Asparouhov and Muthén (2021).
Note that these *p*-values are asymmetric
bootstrap *p*-values based on the
distribution of the bootstrap estimates.
They 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)
dat <- modmed_x1m3w4y1
mod <-
"
m1 ~ a1 * x + d1 * w1 + e1 * x:w1
m2 ~ a2 * x
y ~ b1 * m1 + b2 * m2 + cp * x
"
fit <- sem(mod, dat,
meanstructure = TRUE, fixed.x = FALSE, se = "none", baseline = FALSE)
# Conditional effects from x to m1 when w1 is equal to each of the default levels
cond_indirect_effects(x = "x", y = "m1",
wlevels = "w1", fit = fit)
#>
#> == Conditional effects ==
#>
#> Path: x -> m1
#> Conditional on moderator(s): w1
#> Moderator(s) represented by: w1
#>
#> [w1] (w1) ind m1~x
#> 1 M+1.0SD 1.228 0.750 0.750
#> 2 Mean 0.259 0.523 0.523
#> 3 M-1.0SD -0.710 0.297 0.297
#>
#> - The 'ind' column shows the effects.
#> - ‘m1~x’ is/are the path coefficient(s) along the path conditional on
#> the moderator(s).
#>
# Conditional Indirect effect from x1 through m1 to y,
# when w1 is equal to each of the default levels
out <- cond_indirect_effects(x = "x", y = "y", m = "m1",
wlevels = "w1", fit = fit)
out
#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m1 -> y
#> Conditional on moderator(s): w1
#> Moderator(s) represented by: w1
#>
#> [w1] (w1) ind m1~x y~m1
#> 1 M+1.0SD 1.228 -0.031 0.750 -0.042
#> 2 Mean 0.259 -0.022 0.523 -0.042
#> 3 M-1.0SD -0.710 -0.012 0.297 -0.042
#>
#> - The 'ind' column shows the indirect effects.
#> - ‘m1~x’,‘y~m1’ is/are the path coefficient(s) along the path
#> conditional on the moderator(s).
#>
print(out, digits = 5)
#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m1 -> y
#> Conditional on moderator(s): w1
#> Moderator(s) represented by: w1
#>
#> [w1] (w1) ind m1~x y~m1
#> 1 M+1.0SD 1.22806 -0.03147 0.74988 -0.04197
#> 2 Mean 0.25900 -0.02196 0.52332 -0.04197
#> 3 M-1.0SD -0.71006 -0.01246 0.29676 -0.04197
#>
#> - The 'ind' column shows the indirect effects.
#> - ‘m1~x’,‘y~m1’ is/are the path coefficient(s) along the path
#> conditional on the moderator(s).
#>
print(out, annotation = FALSE)
#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m1 -> y
#> Conditional on moderator(s): w1
#> Moderator(s) represented by: w1
#>
#> [w1] (w1) ind m1~x y~m1
#> 1 M+1.0SD 1.228 -0.031 0.750 -0.042
#> 2 Mean 0.259 -0.022 0.523 -0.042
#> 3 M-1.0SD -0.710 -0.012 0.297 -0.042
```