A parallel mediation model.
Format
A data frame with 100 rows and 6 variables:
- x
Predictor. Numeric.
- m1
Mediator 1. Numeric.
- m2
Mediator 2. Numeric.
- y
Outcome variable. Numeric.
- c1
Control variable. Numeric.
- c2
Control variable. Numeric.
Examples
library(lavaan)
data(data_parallel)
mod <-
"
m1 ~ a1 * x + c1 + c2
m2 ~ a2 * x + c1 + c2
y ~ b2 * m2 + b1 * m1 + x + c1 + c2
indirect1 := a1 * b1
indirect2 := a2 * b2
indirect := a1 * b1 + a2 * b2
"
fit <- sem(mod, data_parallel,
meanstructure = TRUE, fixed.x = FALSE)
parameterEstimates(fit)[c(1, 4, 7, 8, 27:29), ]
#> lhs op rhs label est se z pvalue ci.lower
#> 1 m1 ~ x a1 0.877 0.112 7.823 0.000 0.657
#> 4 m2 ~ x a2 0.297 0.108 2.753 0.006 0.086
#> 7 y ~ m2 b2 0.471 0.190 2.484 0.013 0.099
#> 8 y ~ m1 b1 0.486 0.182 2.667 0.008 0.129
#> 27 indirect1 := a1*b1 indirect1 0.427 0.169 2.524 0.012 0.095
#> 28 indirect2 := a2*b2 indirect2 0.140 0.076 1.845 0.065 -0.009
#> 29 indirect := a1*b1+a2*b2 indirect 0.566 0.185 3.058 0.002 0.203
#> ci.upper
#> 1 1.097
#> 4 0.508
#> 7 0.843
#> 8 0.844
#> 27 0.758
#> 28 0.288
#> 29 0.929