View source: R/print_indirect_list.R
print.indirect_list | R Documentation |
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,
...
)
x |
The output of
|
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
|
pvalue |
Logical. If |
pvalue_digits |
Number of decimal places to display for the p-values. Default is 3. |
se |
Logical. If |
for_each_path |
Logical. If
|
... |
Other arguments. If
|
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.
x
is returned invisibly.
Called for its side effect.
Asparouhov, A., & Muthén, B. (2021). Bootstrap p-value computation. Retrieved from https://www.statmodel.com/download/FAQ-Bootstrap%20-%20Pvalue.pdf
many_indirect_effects()
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
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