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Implementation Note: This module defaults to iterative REML/ML at lme4::lmer accuracy. Henderson Method III remains selectable as a fast alternative. Random slopes and crossed/nested random intercepts are supported and cross-checked against lme4, but cannot yet be combined in one model.
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› Mixed Models
Data & Demo
Load your data (CSV) or use built-in demo data — or enter it in another MindStat module and it appears here automatically
DATA SOURCE
ICC Calculator
Intraclass Correlation Coefficient — assess rater agreement or consistency
SETTINGS
Linear Mixed Model (LMM)
Y ~ X + (1 | Group) — choose REML (default, matches lme4) or Henderson III (fast)
⚠ Add a second grouping factor for crossed/nested random intercepts (1|A)+(1|B), or a random-slope covariate for (z|Group) — both at lme4::lmer accuracy. The two cannot yet be combined. For structures beyond these, use the WebR R engine below.
MODEL SPECIFICATION
Select to fit Y ~ X + (1|A) + (1|B) — crossed/nested random intercepts, at lme4::lmer accuracy.

ℹ️ Random intercepts AND random slopes are supported at lme4::lmer accuracy, with Satterthwaite p-values (lmerTest-grade). The WebR Verify button cross-checks against lme4.

Generalized Linear Mixed Model (GLMM)
Y ~ X + (1 | Group) for binary (logistic) or count (Poisson) outcomes — Laplace approximation
⚠ GLMM with Laplace — single random intercept or cross-classified (1|A) + (1|B). Binomial (logit) / Poisson (log). Matches lme4::glmer within ~1%.
MODEL SPECIFICATION
Select to fit Y ~ X + (1|A) + (1|B) — for non-nested factors (e.g. subjects × items).
Linear Growth Curve
Plot individual trajectories and group means over time
SETTINGS
Advanced R Engine
Run full lme4::lmer and nlme in the browser via WebR
WEBR STATUS

Full mixed models (ML/REML) require an R engine. The JavaScript approximations are limited to random-intercept OLS. To run lme4::lmer or nlme::lme, you can load WebR directly in the browser.

ⓘ WebR downloads ~20 MB on first load. Ensure a stable internet connection.