# Confidence Interval of Indirect Effect or Conditional Indirect Effect

Source:`R/confint_indirect.R`

`confint.indirect.Rd`

Return the
confidence interval of the indirect
effect or conditional indirect effect
stored in the output of
`indirect_effect()`

or
`cond_indirect()`

.

## Usage

```
# S3 method for indirect
confint(object, parm, level = 0.95, boot_type, ...)
```

## Arguments

- object
The output of

`indirect_effect()`

or`cond_indirect()`

.- parm
Ignored because the stored object always has only one parameter.

- level
The level of confidence, default is .95, returning the 95% confidence interval.

- boot_type
If bootstrap confidence interval is to be formed, the type of bootstrap confidence interval. The supported types are

`"perc"`

(percentile bootstrap confidence interval, the recommended method) and`"bc"`

(bias-corrected, or BC, bootstrap confidence interval). If not supplied, the stored`boot_type`

will be used.- ...
Additional arguments. Ignored by the function.

## Details

It extracts and returns the stored confidence interval if available.

The type of confidence interval depends on the call used to compute the effect. This function merely retrieves the stored estimates, which could be generated by nonparametric bootstrapping, Monte Carlo simulation, or other methods to be supported in the future, and uses them to form the percentile confidence interval.

## Examples

```
dat <- modmed_x1m3w4y1
# Indirect Effect
library(lavaan)
mod1 <-
"
m1 ~ x
m2 ~ m1
y ~ m2 + x
"
fit <- sem(mod1, dat,
meanstructure = TRUE, fixed.x = FALSE,
se = "none", baseline = FALSE)
# R should be at least 2000 or 5000 in real research.
out1 <- indirect_effect(x = "x", y = "y",
m = c("m1", "m2"),
fit = fit,
boot_ci = TRUE, R = 45, seed = 54151,
parallel = FALSE,
progress = FALSE)
out1
#>
#> == Indirect Effect ==
#>
#> Path: x -> m1 -> m2 -> y
#> Indirect Effect: 0.064
#> 95.0% Bootstrap CI: [-0.052 to 0.156]
#>
#> Computation Formula:
#> (b.m1~x)*(b.m2~m1)*(b.y~m2)
#> Computation:
#> (0.52252)*(0.39883)*(0.30941)
#>
#> Percentile confidence interval formed by nonparametric bootstrapping
#> with 45 bootstrap samples.
#>
#> Coefficients of Component Paths:
#> Path Coefficient
#> m1~x 0.523
#> m2~m1 0.399
#> y~m2 0.309
#>
confint(out1)
#> Percentile: 2.5 % Percentile: 97.5 %
#> y~x -0.05232493 0.1562506
```