# Confidence Intervals of Indirect Effects or Conditional Indirect Effects

Source:`R/confint_cond_indirect_effects.R`

`confint.cond_indirect_effects.Rd`

Return the confidence
intervals of the conditional indirect
effects or conditional effects in the
output of `cond_indirect_effects()`

.

## Usage

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

## Arguments

- object
The output of

`cond_indirect_effects()`

.- parm
Ignored. Always returns the confidence intervals of the effects for all levels stored.

- level
The level of confidence, default is .95, returning the 95% confidence interval. Ignored for now and will use the level of the stored intervals.

- ...
Additional arguments. Ignored by the function.

## Value

A data frame with two
columns, one for each confidence
limit of the confidence intervals.
The number of rows is equal to the
number of rows of `object`

.

## Details

It extracts and returns the columns for confidence intervals, if available.

The type of confidence intervals depends on the call used to compute the effects. This function merely retrieves the confidence intervals stored, if any, which could be formed by nonparametric bootstrapping, Monte Carlo simulation, or other methods to be supported in the future.

## Examples

```
library(lavaan)
dat <- modmed_x1m3w4y1
mod <-
"
m1 ~ x + w1 + x:w1
m2 ~ m1
y ~ m2 + x + w4 + m2:w4
"
fit <- sem(mod, dat, meanstructure = TRUE, fixed.x = FALSE, se = "none", baseline = FALSE)
est <- parameterEstimates(fit)
# Examples for cond_indirect():
# Create levels of w1 and w4
w1levels <- mod_levels("w1", fit = fit)
w1levels
#> w1
#> M+1.0SD 1.2280576
#> Mean 0.2589999
#> M-1.0SD -0.7100578
w4levels <- mod_levels("w4", fit = fit)
w4levels
#> w4
#> M+1.0SD 1.2087784
#> Mean 0.1532493
#> M-1.0SD -0.9022798
w1w4levels <- merge_mod_levels(w1levels, w4levels)
# Conditional effects from x to m1 when w1 is equal to each of the levels
# R should be at least 2000 or 5000 in real research.
out1 <- suppressWarnings(cond_indirect_effects(x = "x", y = "m1",
wlevels = w1levels, fit = fit,
boot_ci = TRUE, R = 20, seed = 54151,
parallel = FALSE,
progress = FALSE))
confint(out1)
#> 2.5 % 97.5 %
#> M+1.0SD 0.5470997 0.9277341
#> Mean 0.4277303 0.5787753
#> M-1.0SD 0.1590855 0.4261894
```