interlace-lme 0.2.4

Joint REML estimation for linear mixed models with crossed random intercepts using sparse design matrices

interlace

Documentation

Pure-Python profiled REML estimation for linear mixed models with crossed random intercepts, validated to match R's lme4::lmer().

Designed as a drop-in replacement for statsmodels.MixedLM in diagnostics pipelines that require crossed grouping factors (e.g. (1|worker) + (1|company)), which statsmodels does not support.

Scope: interlace fits models with random intercepts only — it does not support random slopes, generalised outcomes (GLMM), or nested random effects with / syntax. For those cases, use R's lme4 directly or a Python GLMM library.

Installation

pip install interlace-lme

Requires Python ≥ 3.13.

Quick start

import pandas as pd
from interlace import fit

result = fit(
    formula="score ~ hours_studied + prior_gpa",
    data=df,
    groups=["student_id", "school_id"],   # crossed random intercepts
)

print(result.fe_params)          # fixed-effect coefficients
print(result.variance_components) # per-factor variance components
print(result.scale)              # residual variance σ²

groups accepts a single string (one random intercept) or a list (crossed intercepts). The first entry is the primary grouping factor.

Usage

Fitting

from interlace import fit

result = fit(formula, data, groups, method="REML")

Returns a CrossedLMEResult with the following attributes:

Attribute	Description
fe_params	Fixed-effect coefficients (Series)
fe_bse	Standard errors
fe_pvalues	Wald p-values
fe_conf_int	95% confidence intervals
random_effects	Dict of BLUPs per grouping factor
variance_components	Dict of variance estimates per grouping factor
scale	Residual variance σ²
fittedvalues	Conditional fitted values (Xβ + Zû)
resid	Conditional residuals
llf, aic, bic	Log-likelihood and information criteria

Prediction

# In-sample (uses BLUPs)
result.predict()

# New data (unseen group levels shrink to zero)
result.predict(newdata=df_new)

# Fixed effects only
result.predict(newdata=df_new, include_re=False)

Residuals

from interlace import hlm_resid

resid_df = hlm_resid(result, type="conditional")  # or "marginal"
# Returns DataFrame with .resid, .fitted, and original data columns

Leverage

from interlace import leverage

lev = leverage(result)  # array of hat-matrix diagonal values

Influence diagnostics

from interlace import hlm_influence, cooks_distance, mdffits, n_influential, tau_gap

infl = hlm_influence(result, level=1)   # Cook's D, MDFFITS, COVTRACE, COVRATIO, RVC per obs

# Scalar summaries
n = n_influential(result)   # count of high-influence observations
gap = tau_gap(result)        # gap statistic between influential and non-influential groups

Combined augment

from interlace import hlm_augment

aug = hlm_augment(result)
# DataFrame: original data + conditional residuals + influence statistics

Plotting

from interlace import plot_resid, plot_influence, dotplot_diag

plot_resid(resid_df, type="resid_vs_fitted")  # or "qq"
plot_influence(infl_df, measure="cooks_d")
dotplot_diag(infl_df, variable="cooks_d", cutoff="internal")

All plots return plotnine.ggplot objects.

statsmodels compatibility

CrossedLMEResult exposes the same interface as statsmodels.MixedLMResults so it can be used as a drop-in in downstream code that accesses fe_params, resid, scale, fittedvalues, random_effects, predict(), and model.exog / model.groups / model.data.frame.

hlm_resid, hlm_influence, and hlm_augment all accept either a CrossedLMEResult or a statsmodels MixedLMResults object.

Parity with lme4

Results are validated against R's lme4::lmer() to the following tolerances:

Metric	Tolerance
Fixed effects	abs diff < 1e-4
Variance components	rel diff < 5%
BLUP correlation	> 0.99
Conditional residual correlation	> 0.999

Contributing

Bug reports, documentation fixes, and new features are welcome — see CONTRIBUTING.md for how to get started. To open an issue or ask a question, use the GitHub issue tracker.

Attribution

• lme4 — the reference implementation for mixed-effects models in R; interlace targets parity with lme4::lmer() and uses its output as the validation benchmark.
• HLMdiag — the R package whose diagnostics API (hlm_resid, hlm_influence, hlm_augment, dotplot_diag) interlace replicates in Python.