print.indirect_list function

Print an 'indirect_list' Class Object

Print the content of the output of many_indirect_effects().

## S3 method for class 'indirect_list'
print(
  x,
  digits = 3,
  annotation = TRUE,
  pvalue = FALSE,
  pvalue_digits = 3,
  se = FALSE,
  for_each_path = FALSE,
  ...
)

Arguments

• x: The output of many_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.
• pvalue_digits: Number of decimal places to display for the p-values. Default is 3.
• se: Logical. If TRUE and 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.
• for_each_path: Logical. If TRUE, each of the paths will be printed individually, using the print-method of the output of indirect_effect(). Default is FALSE.
• ...: Other arguments. If for_each_path is TRUE, they will be passed to the print method of the output of indirect_effect(). Ignored otherwise.

Returns

x is returned invisibly. Called for its side effect.

Details

The print method of the indirect_list-class object.

If bootstrapping confidence interval was requested, this method has the option to print a p-value computed by the method presented in Asparouhov and Muthén (2021). Note that this p-value is asymmetric bootstrap p-value based on the distribution of the bootstrap estimates. It is 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.

Examples

library(lavaan)
data(data_serial_parallel)
mod <-
"
m11 ~ x + c1 + c2
m12 ~ m11 + x + c1 + c2
m2 ~ x + c1 + c2
y ~ m12 + m2 + m11 + x + c1 + c2
"
fit <- sem(mod, data_serial_parallel,
           fixed.x = FALSE)
# All indirect paths from x to y
paths <- all_indirect_paths(fit,
                           x = "x",
                           y = "y")
paths
# Indirect effect estimates
out <- many_indirect_effects(paths,
                             fit = fit)
out

References

Asparouhov, A., & Muthén, B. (2021). Bootstrap p-value computation. Retrieved from https://www.statmodel.com/download/FAQ-Bootstrap%20-%20Pvalue.pdf

See Also

many_indirect_effects()