A simple mediation model.
Format
A data frame with 100 rows and 5 variables:
- x
Predictor. Numeric.
- m
Mediator. Numeric.
- y
Outcome variable. Numeric.
- c1
Control variable. Numeric.
- c2
Control variable. Numeric.
Examples
library(lavaan)
data(data_med)
mod <-
"
m ~ a * x + c1 + c2
y ~ b * m + x + c1 + c2
ab := a * b
"
fit <- sem(mod, data_med, fixed.x = FALSE)
parameterEstimates(fit)
#> lhs op rhs label est se z pvalue ci.lower ci.upper
#> 1 m ~ x a 0.935 0.079 11.802 0.000 0.779 1.090
#> 2 m ~ c1 0.198 0.075 2.629 0.009 0.050 0.345
#> 3 m ~ c2 -0.168 0.101 -1.668 0.095 -0.366 0.029
#> 4 y ~ m b 0.785 0.243 3.227 0.001 0.308 1.261
#> 5 y ~ x 0.508 0.298 1.704 0.088 -0.076 1.092
#> 6 y ~ c1 0.140 0.189 0.743 0.458 -0.230 0.511
#> 7 y ~ c2 -0.154 0.249 -0.620 0.535 -0.642 0.334
#> 8 m ~~ m 0.681 0.096 7.071 0.000 0.493 0.870
#> 9 y ~~ y 4.030 0.570 7.071 0.000 2.913 5.147
#> 10 x ~~ x 1.102 0.156 7.071 0.000 0.797 1.408
#> 11 x ~~ c1 0.026 0.116 0.221 0.825 -0.202 0.253
#> 12 x ~~ c2 0.100 0.087 1.143 0.253 -0.071 0.271
#> 13 c1 ~~ c1 1.218 0.172 7.071 0.000 0.880 1.555
#> 14 c1 ~~ c2 -0.092 0.092 -1.002 0.316 -0.272 0.088
#> 15 c2 ~~ c2 0.685 0.097 7.071 0.000 0.495 0.875
#> 16 ab := a*b ab 0.733 0.236 3.112 0.002 0.272 1.195