Confidence Interval for Delta_Med in a 'delta_med'-Class Object

Return the confidence interval of the Delta_Med in the output of delta_med().

Usage

# S3 method for class 'delta_med'
confint(object, parm, level = NULL, boot_type, ...)

Arguments

object: The output of delta_med().
parm: Not used because only one parameter, the Delta_Med, is allowed.
level: The level of confidence, default is NULL and the level used when the object was created will be used.
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.
...: Optional arguments. Ignored.

Value

A one-row matrix of the confidence interval. All values are NA if bootstrap confidence interval was not requested when calling delta_med().

Details

It returns the nonparametric bootstrap percentile confidence interval of Delta_Med, proposed byLiu, Yuan, and Li (2023). The object must be the output of delta_med(), with bootstrap confidence interval requested when calling delta_med(). However, the level of confidence can be different from that used when call delta_med().

Author

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

Examples


library(lavaan)
dat <- data_med
mod <-
"
m ~ x
y ~ m + x
"
fit <- sem(mod, dat)

# Call do_boot() to generate
# bootstrap estimates
# Use 2000 or even 5000 for R in real studies
# Set parallel to TRUE in real studies for faster bootstrapping
boot_out <- do_boot(fit,
                    R = 45,
                    seed = 879,
                    parallel = FALSE,
                    progress = FALSE)
# Remove 'progress = FALSE' in practice
dm_boot <- delta_med(x = "x",
                     y = "y",
                     m = "m",
                     fit = fit,
                     boot_out = boot_out,
                     progress = FALSE)
dm_boot
#> Call:
#> delta_med(x = "x", y = "y", m = "m", fit = fit, boot_out = boot_out, 
#>     progress = FALSE)
#> 
#> Predictor (x)       : x 
#> Mediator(s) (m)     : m 
#> Outcome variable (y): y 
#> 
#> Delta_med                                     :          0.230
#> 95.0% Bootstrap percentile confidence interval: [0.097, 0.318]
#> Number of bootstrap samples                   :             45
#> 
#> Paths removed:
#>  m~x
confint(dm_boot)
#>           Percentile 2.5 % Percentile 97.5 %
#> Delta_Med       0.09725932         0.3175632