A two-moderator model, both categorical.
Format
A data frame with 600 rows and 6 variables:
- y
Outcome variable. Numeric.
- x
Predictor. Numeric.
- gp
Moderator 1. String: "Control" and "Treatment".
- site
Moderator 2. String: "Site 1", "Site 2", and "Site 3".
- c1
Control variable. Numeric.
- c2
Control variable. Numeric.
Examples
data(data_mod_cat_2w)
lm_out <- lm(y ~ x*gp + x*site + c1 + c2, data_mod_cat_2w)
out <- cond_effects(
wlevels = c("site", "gp"),
x = "x",
fit = lm_out
)
out
#>
#> == Conditional effects ==
#>
#> Path: x -> y
#> Conditional on moderator(s): site, gp
#> Moderator(s) represented by: siteSite 2, siteSite 3, gpTreatment
#>
#> [site] [gp] (siteSite 2) (siteSite 3) (gpTreatment) ind SE Stat
#> 1 Site 1 Control 0 0 0 -0.084 0.060 -1.409
#> 2 Site 1 Treatment 0 0 1 0.271 0.060 4.543
#> 3 Site 2 Control 1 0 0 0.229 0.065 3.527
#> 4 Site 2 Treatment 1 0 1 0.584 0.064 9.146
#> 5 Site 3 Control 0 1 0 -0.173 0.067 -2.592
#> 6 Site 3 Treatment 0 1 1 0.182 0.062 2.936
#> pvalue Sig CI.lo CI.hi
#> 1 0.159 -0.201 0.033
#> 2 0.000 *** 0.154 0.388
#> 3 0.000 *** 0.101 0.356
#> 4 0.000 *** 0.459 0.709
#> 5 0.010 ** -0.304 -0.042
#> 6 0.003 ** 0.060 0.304
#>
#> - [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 = "site", graph_type = "tumble")
