Print a 'cond_indirect_effects' Class Object
Source:R/print_cond_indirect_effect.R
print.cond_indirect_effects.RdPrint the content of the
output of cond_indirect_effects()
Usage
# S3 method for cond_indirect_effects
print(
x,
digits = 3,
annotation = TRUE,
pvalue = FALSE,
pvalue_digits = 3,
se = FALSE,
...
)Arguments
- x
The output of
cond_indirect_effects().- digits
Number of digits to display. Default is 3.
- annotation
Logical. Whether the annotation after the table of effects is to be printed. Default is
TRUE.- pvalue
Logical. If
TRUE, asymmetric p-values based on bootstrapping will be printed if available. Default isFALSE.- pvalue_digits
Number of decimal places to display for the p-values. Default is 3.
- se
Logical. If
TRUEand confidence intervals are available, the standard errors of the estimates are also printed. They are simply the standard deviations of the bootstrap estimates or Monte Carlo simulated values, depending on the method used to form the confidence intervals.- ...
Other arguments. Not used.
Details
The print method of the
cond_indirect_effects-class object.
If bootstrapping confidence intervals were requested, this method has the option to print p-values computed by the method presented in Asparouhov and Muthén (2021). Note that these p-values are asymmetric bootstrap p-values based on the distribution of the bootstrap estimates. They not computed based on the distribution under the null hypothesis.
For a p-value of a, it means that a 100(1 - a)% bootstrapping confidence interval will have one of its limits equal to 0. A confidence interval with a higher confidence level will include zero, while a confidence interval with a lower confidence level will exclude zero.
References
Asparouhov, A., & Muthén, B. (2021). Bootstrap p-value computation. Retrieved from https://www.statmodel.com/download/FAQ-Bootstrap%20-%20Pvalue.pdf
Examples
library(lavaan)
dat <- modmed_x1m3w4y1
mod <-
"
m1 ~ a1 * x + d1 * w1 + e1 * x:w1
m2 ~ a2 * x
y ~ b1 * m1 + b2 * m2 + cp * x
"
fit <- sem(mod, dat,
meanstructure = TRUE, fixed.x = FALSE, se = "none", baseline = FALSE)
# Conditional effects from x to m1 when w1 is equal to each of the default levels
cond_indirect_effects(x = "x", y = "m1",
wlevels = "w1", fit = fit)
#>
#> == Conditional effects ==
#>
#> Path: x -> m1
#> Conditional on moderator(s): w1
#> Moderator(s) represented by: w1
#>
#> [w1] (w1) ind m1~x
#> 1 M+1.0SD 1.228 0.750 0.750
#> 2 Mean 0.259 0.523 0.523
#> 3 M-1.0SD -0.710 0.297 0.297
#>
#> - The 'ind' column shows the effects.
#> - ‘m1~x’ is/are the path coefficient(s) along the path conditional on
#> the moderator(s).
#>
# Conditional Indirect effect from x1 through m1 to y,
# when w1 is equal to each of the default levels
out <- cond_indirect_effects(x = "x", y = "y", m = "m1",
wlevels = "w1", fit = fit)
out
#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m1 -> y
#> Conditional on moderator(s): w1
#> Moderator(s) represented by: w1
#>
#> [w1] (w1) ind m1~x y~m1
#> 1 M+1.0SD 1.228 -0.031 0.750 -0.042
#> 2 Mean 0.259 -0.022 0.523 -0.042
#> 3 M-1.0SD -0.710 -0.012 0.297 -0.042
#>
#> - The 'ind' column shows the indirect effects.
#> - ‘m1~x’,‘y~m1’ is/are the path coefficient(s) along the path
#> conditional on the moderator(s).
#>
print(out, digits = 5)
#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m1 -> y
#> Conditional on moderator(s): w1
#> Moderator(s) represented by: w1
#>
#> [w1] (w1) ind m1~x y~m1
#> 1 M+1.0SD 1.22806 -0.03147 0.74988 -0.04197
#> 2 Mean 0.25900 -0.02196 0.52332 -0.04197
#> 3 M-1.0SD -0.71006 -0.01246 0.29676 -0.04197
#>
#> - The 'ind' column shows the indirect effects.
#> - ‘m1~x’,‘y~m1’ is/are the path coefficient(s) along the path
#> conditional on the moderator(s).
#>
print(out, annotation = FALSE)
#>
#> == Conditional indirect effects ==
#>
#> Path: x -> m1 -> y
#> Conditional on moderator(s): w1
#> Moderator(s) represented by: w1
#>
#> [w1] (w1) ind m1~x y~m1
#> 1 M+1.0SD 1.228 -0.031 0.750 -0.042
#> 2 Mean 0.259 -0.022 0.523 -0.042
#> 3 M-1.0SD -0.710 -0.012 0.297 -0.042