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Coefficient Table of an 'indirect_list' Class Object

Source: R/print_indirect_list.R
indirect_effects_from_list.Rd

Create a coefficient table for the point estimates and confidence intervals (if available) in the output of many_indirect_effects().

Usage

indirect_effects_from_list(object, add_sig = TRUE, pvalue = FALSE, se = FALSE)

Arguments

object

The output of many_indirect_effects() or other functions that return an object of the class indirect_list.

add_sig

Whether a column of significance test results will be added. Default is TRUE.

pvalue

Logical. If TRUE, asymmetric p-values based on bootstrapping will be added available. Default is FALSE.

se

Logical. If TRUE and confidence intervals are available, the standard errors of the estimates are also added. 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.

Value

A data frame with the indirect effect estimates and confidence intervals (if available). It also has A string column, "Sig", for #' significant test results if add_sig is TRUE and confidence intervals are available.

Details

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

See also

many_indirect_effects()

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
#> Call: 
#> all_indirect_paths(fit = fit, x = "x", y = "y")
#> Path(s): 
#>   path                
#> 1 x -> m11 -> m12 -> y
#> 2 x -> m11 -> y       
#> 3 x -> m12 -> y       
#> 4 x -> m2 -> y        

# Indirect effect estimates
out <- many_indirect_effects(paths,
                             fit = fit)
out
#> 
#> ==  Indirect Effect(s)   ==
#>                        ind
#> x -> m11 -> m12 -> y 0.193
#> x -> m11 -> y        0.163
#> x -> m12 -> y        0.059
#> x -> m2 -> y         0.364
#> 
#>  - The 'ind' column shows the indirect effects.
#>  

# Create a data frame of the indirect effect estimates

out_df <- indirect_effects_from_list(out)
out_df
#>                             ind
#> x -> m11 -> m12 -> y 0.19321379
#> x -> m11 -> y        0.16261213
#> x -> m12 -> y        0.05946653
#> x -> m2 -> y         0.36440188