mlmoderator-package: mlmoderator: Probing and Visualizing Multilevel Interaction...

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

mlmoderator: Probing and Visualizing Multilevel Interaction Effects

Description

mlmoderator provides a unified workflow for estimating, probing, and visualizing multilevel moderation effects from mixed-effects models fitted with lme4::lmer(). It supports simple slopes analysis, Johnson—Neyman intervals, publication-ready interaction plots, and grand- or group-mean centering.

Core functions

Function	Description
mlm_center()	Grand- or group-mean center variables
mlm_probe()	Compute simple slopes at selected moderator values
mlm_jn()	Johnson-Neyman significance regions
mlm_plot()	Publication-ready interaction plot
mlm_summary()	Consolidated moderation report
mlm_variance_decomp()	Decompose slope uncertainty into fixed + random components
mlm_surface()	Slope surface heatmap over the full predictor x moderator space
mlm_sensitivity()	Robustness of interaction to ICC shift and omitted confounders

Typical workflow

library(mlmoderator)
library(lme4)

data(school_data)

# 1. Center variables
dat <- mlm_center(school_data, vars = "ses", cluster = "school", type = "group")

# 2. Fit model
mod <- lmer(math ~ ses * climate + gender + (1 + ses | school), data = dat)

# 3. Probe interaction
mlm_probe(mod, pred = "ses", modx = "climate")

# 4. Johnson-Neyman interval
mlm_jn(mod, pred = "ses", modx = "climate")

# 5. Plot
mlm_plot(mod, pred = "ses", modx = "climate")

# 6. Summary report
mlm_summary(mod, pred = "ses", modx = "climate")

Author(s)

Maintainer: Subir Hait [e-mail] (ORCID)

See Also

Useful links:

• https://github.com/causalfragility-lab/mlmoderator

• Report bugs at https://github.com/causalfragility-lab/mlmoderator/issues

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