Plotting mediation effects and contrasts

Reproducible two-condition data

set.seed(918)
n <- 350
w <- rnorm(n, 3, 2)
avg1 <- rnorm(n); avg2 <- rnorm(n)
dm1 <- 2 + .8*w + rnorm(n)
dm2 <- 1 + .4*w + .3*dm1 + rnorm(n)
dy <- .5 + .6*w + .5*dm1 + .7*dm2 + .2*dm1*w + .3*avg1 + rnorm(n)
y1 <- rnorm(n)
dat <- data.frame(M11=avg1-dm1/2, M12=avg1+dm1/2,
                  M21=avg2-dm2/2, M22=avg2+dm2/2,
                  Y1=y1, Y2=y1+dy, W=w)
dat$Group <- cut(w, c(-Inf,2,4,Inf), labels=c("A","B","C"))
fit_args <- list(data=dat, M_C1=c("M11","M21"), M_C2=c("M12","M22"),
                 Y_C1="Y1", Y_C2="Y2", form="P", Na="DE", ci_method="mc",
                 R=2000, seed=2026, standardized=TRUE, verbose=FALSE)
plain <- do.call(wsMed, fit_args)
continuous <- do.call(wsMed, c(fit_args,
  list(W="W", W_type="continuous", MP=c("b1","d1"))))
categorical <- do.call(wsMed, c(fit_args,
  list(W="Group", W_type="categorical", MP=c("b1","d1"))))

These separate fits illustrate each plotting interface; changing W from continuous to grouped changes the fitted model and is not a transformation of the original conditional estimates.

Forest plots

plot_effects() displays complete indirect effects, their total, the direct effect (cp) and the total effect. Choose rows and their display order with paths; use a named labels vector for readable descriptions.

forest <- plot_effects(plain, standardized=TRUE,
  paths=c("indirect_1", "indirect_2", "total_indirect", "cp", "total_effect"),
  labels=c(indirect_1="Via M1", indirect_2="Via M2",
           total_indirect="Total indirect", cp="Direct", total_effect="Total"))
forest

plot of chunk forest

With a moderator, this forest shows effects at the model reference value (centered continuous W = 0, or the reference category). It does not average conditional effects across W. To display different W levels, use the next plot.

Conditional effects by group or selected level

group_plot <- plot_conditional_effects(categorical, standardized=TRUE,
  paths=c("indirect_1","indirect_2"),
  labels=c(indirect_1="Via M1",indirect_2="Via M2"), ncol=2)
group_plot

plot of chunk categorical-effects

Each point and interval belongs to a fitted group. There are no interpolating lines between categories. levels can select and reorder group labels. Choose type = "paths" for available path coefficients or type = "overall" for total indirect and total effects. All panels share their effect axis.

The same function can display the stored reference levels of a continuous W:

plot_conditional_effects(continuous, paths="indirect_effect_1",
  levels=c("-1 SD","0 SD","+1 SD"), standardized=TRUE,
  labels=c(indirect_effect_1="Via M1"))

plot of chunk continuous-levels

The axis includes raw W values. indirect_1 and indirect_effect_1 are accepted as aliases when selecting an available indirect effect; the underlying result tables keep their existing identifiers.

Differences need their own intervals

One effect having an interval that excludes zero and another having an interval that includes zero does not establish a difference between them. plot_contrasts() displays the difference and its own interval instead.

contrast_plot <- plot_contrasts(categorical, standardized=TRUE,
  paths=c("indirect_1","indirect_2"),
  labels=c(indirect_1="Via M1",indirect_2="Via M2"), ncol=2)
contrast_plot

plot of chunk categorical-contrasts

For example, B - A means the conditional effect in B minus that in A. With a continuous moderator, the same function compares stored low, mean and high moderator levels. contrasts selects exact labels from the result table (also visible in contrast_plot$data$Contrast). Conditional contrasts use the already-computed joint-draw contrast intervals, not differences between endpoints of separate intervals.

Without a moderator, the plot compares indirect paths with one another:

plot_contrasts(plain, standardized=TRUE)

plot of chunk path-contrasts

Here the helper uses stored joint draws and fitted/pooled point estimates. Standardized contrasts are computed with each draw’s own endpoint scales before forming percentile intervals. No model is refitted. A moderator fit currently supports contrasts within each effect across groups/levels, not arbitrary contrasts between different indirect paths at a fixed W. For continuous moderators, type = "overall" is unavailable unless an overall contrast table is present; specific indirect and path contrasts remain available.

Conditional curves and their highlighted ranges

plot_moderation_curve(continuous, "total_indirect", standardized=TRUE)

plot of chunk curve

The band uses the fitted confidence level. Shading denotes stored grid values whose pointwise interval excludes zero. Labels give raw W bounds, not sample percentiles. Boundaries are grid approximations; they are not exact Johnson–Neyman boundaries or simultaneous confidence regions. An adjacent change from a negative to a positive interval breaks a highlighted range.

Inspect and export

forest$data[, c("Path","Estimate","CI.LL","CI.UL")]
#>              Path   Estimate      CI.LL      CI.UL
#> 16     indirect_1  1.8547450  1.7256431  1.9948219
#> 17     indirect_2  1.0906147  0.9887227  1.1976153
#> 18 total_indirect  2.9453597  2.7781650  3.1264568
#> 1              cp -0.7473749 -0.8593807 -0.6275049
#> 19   total_effect  2.1979847  2.0356903  2.3746074
attr(forest, "wsmed_plot")
#> $engine
#> [1] "mc"
#> 
#> $standardized
#> [1] TRUE
#> 
#> $level
#> [1] 0.95

ggplot2::ggsave("mediation-effects.pdf", forest, width=8, height=5)
ggplot2::ggsave("group-effects.png", group_plot, width=8, height=5, dpi=300)

All conditional intervals are percentile intervals; bootstrap parameter forest plots use the stored bootstrap interval type. The common marginal scale, fixed raw W probes and joint uncertainty are described in Standardized moderated mediation.