A two-moderator model,
with variation in the standard deviation
of x related to one moderator.
Format
A data frame with 200 rows and 6 variables:
- y
Outcome variable. Numeric.
- x
Predictor. Numeric.
- w1
Moderator 1. Numeric.
- w2
Moderator 2. Numeric.
- c1
Control variable. Numeric.
- c2
Control variable. Numeric.
Examples
library(lavaan)
data(data_mod_2w)
lm_out <- lm(y ~ x*w1 + x*w2 + c1 + c2, data_mod_2w)
out <- cond_effects(
wlevels = c("w1", "w2"),
x = "x",
fit = lm_out
)
out
#>
#> == Conditional effects ==
#>
#> Path: x -> y
#> Conditional on moderator(s): w1, w2
#> Moderator(s) represented by: w1, w2
#>
#> [w1] [w2] (w1) (w2) ind SE Stat pvalue Sig CI.lo CI.hi
#> 1 M+1.0SD M+1.0SD 7.942 4.829 0.528 0.122 4.343 0.000 *** 0.288 0.768
#> 2 M+1.0SD M-1.0SD 7.942 2.897 0.455 0.141 3.220 0.002 ** 0.176 0.734
#> 3 M-1.0SD M+1.0SD 5.813 4.829 0.121 0.163 0.744 0.458 -0.201 0.444
#> 4 M-1.0SD M-1.0SD 5.813 2.897 0.048 0.159 0.303 0.762 -0.266 0.363
#>
#> - [SE] are regression standard errors.
#> - [Stat] are the t statistics used to test the effects.
#> - [pvalue] are p-values computed from 'Stat'.
#> - [Sig]: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘ ’ 1.
#> - [CI.lo to CI.hi] are 95.0% confidence interval computed from
#> regression standard errors.
#> - The 'ind' column shows the conditional effects.
#>
plot(out, facet_grid_cols = "w1", graph_type = "tumble")
plot(out, facet_grid_cols = "w2", graph_type = "tumble")
