Print the content of the
output of indirect_effect() or
cond_indirect().
Usage
# S3 method for indirect
print(x, digits = 3, pvalue = FALSE, pvalue_digits = 3, se = FALSE, ...)Arguments
- x
The output of
indirect_effect()orcond_indirect().- digits
Number of digits to display. Default is 3.
- pvalue
Logical. If
TRUE, asymmetric p-value based on bootstrapping will be printed if available.- pvalue_digits
Number of decimal places to display for the p-value. Default is 3.
- se
Logical. If
TRUEand confidence interval is available, the standard error of the estimate is also printed. This is simply the standard deviation of the bootstrap estimates or Monte Carlo simulated values, depending on the method used to form the confidence interval.- ...
Other arguments. Not used.
Details
The print method of the
indirect-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.
We recommend using confidence interval
directly. Therefore, p-value is not
printed by default. Nevertheless,
users who need it can request it
by setting pvalue to TRUE.
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 + b1 * w1 + d1 * x:w1
m2 ~ a2 * m1 + b2 * w2 + d2 * m1:w2
m3 ~ a3 * m2 + b3 * w3 + d3 * m2:w3
y ~ a4 * m3 + b4 * w4 + d4 * m3:w4
"
fit <- sem(mod, dat,
meanstructure = TRUE, fixed.x = FALSE,
se = "none", baseline = FALSE)
est <- parameterEstimates(fit)
wvalues <- c(w1 = 5, w2 = 4, w3 = 2, w4 = 3)
indirect_1 <- cond_indirect(x = "x", y = "y",
m = c("m1", "m2", "m3"),
fit = fit,
wvalues = wvalues)
indirect_1
#>
#> == Conditional Indirect Effect ==
#>
#> Path: x -> m1 -> m2 -> m3 -> y
#> Moderators: w1, w2, w3, w4
#> Conditional Indirect Effect: 1.176
#> When: w1 = 5.000, w2 = 4.000, w3 = 2.000, w4 = 3.000
#>
#> Computation Formula:
#> (b.m1~x + (b.x:w1)*(w1))*(b.m2~m1 + (b.m1:w2)*(w2))*(b.m3~m2 + (b.m2:w3)*(w3))*(b.y~m3 + (b.m3:w4)*(w4))
#> Computation:
#> ((0.46277) + (0.23380)*(5.00000))*((0.36130) + (0.13284)*(4.00000))*((0.68691) + (0.10880)*(2.00000))*((0.40487) + (0.16260)*(3.00000))
#> Coefficients of Component Paths:
#> Path Conditional Effect Original Coefficient
#> m1~x 1.632 0.463
#> m2~m1 0.893 0.361
#> m3~m2 0.905 0.687
#> y~m3 0.893 0.405
#>
dat <- modmed_x1m3w4y1
mod2 <-
"
m1 ~ a1 * x
m2 ~ a2 * m1
m3 ~ a3 * m2
y ~ a4 * m3 + x
"
fit2 <- sem(mod2, dat,
meanstructure = TRUE, fixed.x = FALSE,
se = "none", baseline = FALSE)
est <- parameterEstimates(fit)
indirect_2 <- indirect_effect(x = "x", y = "y",
m = c("m1", "m2", "m3"),
fit = fit2)
indirect_2
#>
#> == Indirect Effect ==
#>
#> Path: x -> m1 -> m2 -> m3 -> y
#> Indirect Effect: 0.071
#>
#> Computation Formula:
#> (b.m1~x)*(b.m2~m1)*(b.m3~m2)*(b.y~m3)
#> Computation:
#> (0.52252)*(0.39883)*(0.80339)*(0.42610)
#> Coefficients of Component Paths:
#> Path Coefficient
#> m1~x 0.523
#> m2~m1 0.399
#> m3~m2 0.803
#> y~m3 0.426
#>
print(indirect_2, digits = 5)
#>
#> == Indirect Effect ==
#>
#> Path: x -> m1 -> m2 -> m3 -> y
#> Indirect Effect: 0.07134
#>
#> Computation Formula:
#> (b.m1~x)*(b.m2~m1)*(b.m3~m2)*(b.y~m3)
#> Computation:
#> (0.52252)*(0.39883)*(0.80339)*(0.42610)
#> Coefficients of Component Paths:
#> Path Coefficient
#> m1~x 0.52252
#> m2~m1 0.39883
#> m3~m2 0.80339
#> y~m3 0.42610
#>