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Print a 'delta_med' Class Object

Source: R/print_delta_med.R
print.delta_med.Rd

Print the content of a delta_med-class object.

Usage

# S3 method for delta_med
print(x, digits = 3, level = NULL, full = FALSE, boot_type, ...)

Arguments

x

A delta_med-class object.

digits

The number of digits after the decimal. Default is 3.

level

The level of confidence of bootstrap confidence interval, if requested when created. If NULL, the default, the level requested when calling delta_med() is used. If not null, then this level will be used.

full

Logical. Whether additional information will be printed. Default is FALSE.

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

x is returned invisibly. Called for its side effect.

Details

It prints the output of delta_med(), which is a delta_med-class object.

See also

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)
dm <- delta_med(x = "x",
                y = "y",
                m = "m",
                fit = fit)
dm
#> Call:
#> delta_med(x = "x", y = "y", m = "m", fit = fit)
#> 
#> Predictor (x)       : x 
#> Mediator(s) (m)     : m 
#> Outcome variable (y): y 
#> 
#> Delta_med: 0.230
#> 
#> Paths removed:
#>  m~x
print(dm, full = TRUE)
#> Call:
#> delta_med(x = "x", y = "y", m = "m", fit = fit)
#> 
#> Predictor (x)       : x 
#> Mediator(s) (m)     : m 
#> Outcome variable (y): y 
#> 
#> Delta_med: 0.230
#> 
#> Paths removed:
#>  m~x
#> 
#> Additional information:
#> R-sq: Original                            : 0.351
#> R-sq: Mediator(s) removed                 : 0.121
#> Variance of y                             : 6.273
#> Variance of predicted y                   : 2.203
#> Variance of predicted: mediator(s) removed: 0.759

# 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
confint(dm_boot,
        level = .90)
#>           Percentile 5 % Percentile 95 %
#> Delta_Med       0.121015        0.294301