mlm_summary: Summary table for a multilevel moderation effect

In mlmoderator: Probing, Plotting, and Interpreting Multilevel Interaction Effects

Summary table for a multilevel moderation effect

Description

Returns a consolidated summary of the moderation effect: the focal interaction coefficient, simple slopes at selected moderator values, and (optionally) the Johnson—Neyman interval. Designed for quick reporting and results sections.

Usage

mlm_summary(
  model,
  pred,
  modx,
  modx.values = c("mean-sd", "quartiles", "tertiles", "custom"),
  at = NULL,
  conf.level = 0.95,
  jn = TRUE,
  alpha = 0.05
)

Arguments

model	An lmerMod object with a two-way interaction between pred and modx.
pred	Character scalar. Focal predictor name.
modx	Character scalar. Moderator name.
modx.values	Moderator value strategy. See mlm_probe().
at	Optional numeric vector of custom moderator values.
conf.level	Confidence level. Default 0.95.
jn	Logical. Include Johnson-Neyman region? Default TRUE.
alpha	Alpha for JN interval. Default 0.05.

Value

An object of class mlm_summary (a list) with components:

• interaction: one-row data frame for the interaction term.

• simple_slopes: data frame from mlm_probe().

• jn: output of mlm_jn() (or NULL if jn = FALSE).

• Other metadata.

Examples

set.seed(1)
dat <- data.frame(
  y   = rnorm(200), x = rnorm(200),
  m   = rep(rnorm(20), each = 10),
  grp = factor(rep(1:20, each = 10))
)
dat$y <- dat$y + dat$x * dat$m
mod <- lme4::lmer(y ~ x * m + (1 | grp), data = dat,
                  control = lme4::lmerControl(optimizer = "bobyqa"))
mlm_summary(mod, pred = "x", modx = "m")

mlmoderator documentation built on April 4, 2026, 1:07 a.m.