A simple mediation model.
Format
A data frame with 100 rows and 5 variables:
- x
Predictor. Numeric.
- m
Mediator. Numeric.
- y
Outcome variable. Numeric.
- city
Group variable: "City A" or "City B". String.
Examples
library(lavaan)
data(simple_mediation)
mod <-
"
m ~ a * x
y ~ b * m + x
ab := a * b
"
fit <- sem(mod, simple_mediation, fixed.x = FALSE)
parameterEstimates(fit)
#> lhs op rhs label est se z pvalue ci.lower ci.upper
#> 1 m ~ x a 0.374 0.171 2.182 0.029 0.038 0.710
#> 2 y ~ m b 0.375 0.173 2.176 0.030 0.037 0.714
#> 3 y ~ x 0.096 0.303 0.318 0.751 -0.497 0.689
#> 4 m ~~ m 2.777 0.393 7.071 0.000 2.007 3.547
#> 5 y ~~ y 8.272 1.170 7.071 0.000 5.979 10.565
#> 6 x ~~ x 0.946 0.134 7.071 0.000 0.684 1.209
#> 7 ab := a*b ab 0.140 0.091 1.541 0.123 -0.038 0.319
mod_gp <-
"
m ~ c(a1, a2) * x
y ~ c(b1, b2) * m + x
a1b1 := a1 * b1
a2b2 := a2 * b2
ab_diff := a1b1 - a2b2
"
fit_gp <- sem(mod_gp, simple_mediation, fixed.x = FALSE, group = "city")
parameterEstimates(fit_gp)
#> lhs op rhs block group label est se z pvalue ci.lower
#> 1 m ~ x 1 1 a1 0.162 0.229 0.708 0.479 -0.287
#> 2 y ~ m 1 1 b1 0.177 0.243 0.728 0.466 -0.300
#> 3 y ~ x 1 1 0.305 0.376 0.809 0.418 -0.433
#> 4 m ~~ m 1 1 2.382 0.502 4.743 0.000 1.398
#> 5 y ~~ y 1 1 6.354 1.340 4.743 0.000 3.729
#> 6 x ~~ x 1 1 1.008 0.212 4.743 0.000 0.591
#> 7 m ~1 1 1 12.062 1.200 10.049 0.000 9.709
#> 8 y ~1 1 1 2.075 3.531 0.588 0.557 -4.845
#> 9 x ~1 1 1 5.140 0.150 34.349 0.000 4.846
#> 10 m ~ x 2 2 a2 0.678 0.235 2.879 0.004 0.216
#> 11 y ~ m 2 2 b2 0.556 0.257 2.166 0.030 0.053
#> 12 y ~ x 2 2 -0.199 0.481 -0.413 0.680 -1.141
#> 13 m ~~ m 2 2 2.656 0.507 5.244 0.000 1.663
#> 14 y ~~ y 2 2 9.626 1.836 5.244 0.000 6.028
#> 15 x ~~ x 2 2 0.871 0.166 5.244 0.000 0.546
#> 16 m ~1 2 2 10.336 1.175 8.794 0.000 8.032
#> 17 y ~1 2 2 -0.474 3.471 -0.137 0.891 -7.276
#> 18 x ~1 2 2 4.904 0.126 38.964 0.000 4.657
#> 19 a1b1 := a1*b1 0 0 a1b1 0.029 0.057 0.508 0.612 -0.082
#> 20 a2b2 := a2*b2 0 0 a2b2 0.377 0.218 1.731 0.083 -0.050
#> 21 ab_diff := a1b1-a2b2 0 0 ab_diff -0.348 0.225 -1.547 0.122 -0.789
#> ci.upper
#> 1 0.612
#> 2 0.655
#> 3 1.042
#> 4 3.366
#> 5 8.979
#> 6 1.424
#> 7 14.414
#> 8 8.995
#> 9 5.433
#> 10 1.139
#> 11 1.059
#> 12 0.744
#> 13 3.649
#> 14 13.223
#> 15 1.197
#> 16 12.640
#> 17 6.328
#> 18 5.150
#> 19 0.140
#> 20 0.804
#> 21 0.093