Confidence Interval of Indirect Effect or Conditional Indirect Effect

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, ...)

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.
...: Additional arguments. Ignored by the function.

Value

A numeric vector of two elements, the limits of the confidence interval.

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)
#>           2.5 %    97.5 %
#> y~x -0.05232493 0.1562506