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

or
`cond_indirect()`

.

## Usage

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

## Arguments

- x
The output of

`indirect_effect()`

or`cond_indirect()`

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

- pvalue
Logical. If

`TRUE`

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

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

`TRUE`

and confidence interval is available, the standard error of the estimate is also printed. This is simply the standard deviation of the bootstrap estimates or Monte Carlo simulated values, depending on the method used to form the confidence interval.- ...
Other arguments. Not used.

## Details

The `print`

method of the
`indirect`

-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.

We recommend using confidence interval
directly. Therefore, *p*-value is not
printed by default. Nevertheless,
users who need it can request it
by setting `pvalue`

to `TRUE`

.

## 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 + b1 * w1 + d1 * x:w1
m2 ~ a2 * m1 + b2 * w2 + d2 * m1:w2
m3 ~ a3 * m2 + b3 * w3 + d3 * m2:w3
y ~ a4 * m3 + b4 * w4 + d4 * m3:w4
"
fit <- sem(mod, dat,
meanstructure = TRUE, fixed.x = FALSE,
se = "none", baseline = FALSE)
est <- parameterEstimates(fit)
wvalues <- c(w1 = 5, w2 = 4, w3 = 2, w4 = 3)
indirect_1 <- cond_indirect(x = "x", y = "y",
m = c("m1", "m2", "m3"),
fit = fit,
wvalues = wvalues)
indirect_1
#>
#> == Conditional Indirect Effect ==
#>
#> Path: x -> m1 -> m2 -> m3 -> y
#> Moderators: w1, w2, w3, w4
#> Conditional Indirect Effect: 1.176
#> When: w1 = 5.000, w2 = 4.000, w3 = 2.000, w4 = 3.000
#>
#> Computation Formula:
#> (b.m1~x + (b.x:w1)*(w1))*(b.m2~m1 + (b.m1:w2)*(w2))*(b.m3~m2 + (b.m2:w3)*(w3))*(b.y~m3 + (b.m3:w4)*(w4))
#> Computation:
#> ((0.46277) + (0.23380)*(5.00000))*((0.36130) + (0.13284)*(4.00000))*((0.68691) + (0.10880)*(2.00000))*((0.40487) + (0.16260)*(3.00000))
#> Coefficients of Component Paths:
#> Path Conditional Effect Original Coefficient
#> m1~x 1.632 0.463
#> m2~m1 0.893 0.361
#> m3~m2 0.905 0.687
#> y~m3 0.893 0.405
#>
dat <- modmed_x1m3w4y1
mod2 <-
"
m1 ~ a1 * x
m2 ~ a2 * m1
m3 ~ a3 * m2
y ~ a4 * m3 + x
"
fit2 <- sem(mod2, dat,
meanstructure = TRUE, fixed.x = FALSE,
se = "none", baseline = FALSE)
est <- parameterEstimates(fit)
indirect_2 <- indirect_effect(x = "x", y = "y",
m = c("m1", "m2", "m3"),
fit = fit2)
indirect_2
#>
#> == Indirect Effect ==
#>
#> Path: x -> m1 -> m2 -> m3 -> y
#> Indirect Effect 0.071
#>
#> Computation Formula:
#> (b.m1~x)*(b.m2~m1)*(b.m3~m2)*(b.y~m3)
#> Computation:
#> (0.52252)*(0.39883)*(0.80339)*(0.42610)
#> Coefficients of Component Paths:
#> Path Coefficient
#> m1~x 0.523
#> m2~m1 0.399
#> m3~m2 0.803
#> y~m3 0.426
#>
print(indirect_2, digits = 5)
#>
#> == Indirect Effect ==
#>
#> Path: x -> m1 -> m2 -> m3 -> y
#> Indirect Effect 0.07134
#>
#> Computation Formula:
#> (b.m1~x)*(b.m2~m1)*(b.m3~m2)*(b.y~m3)
#> Computation:
#> (0.52252)*(0.39883)*(0.80339)*(0.42610)
#> Coefficients of Component Paths:
#> Path Coefficient
#> m1~x 0.52252
#> m2~m1 0.39883
#> m3~m2 0.80339
#> y~m3 0.42610
#>
```