Package index • mixeff

Fitting models

Construct and fit linear and generalized linear mixed models.

lmm(): Fit a linear mixed-effects model
glmm(): Fit a generalized linear mixed model
mm_negative_binomial(): Negative-binomial family for glmm()
supported_models(): The supported model envelope
compile_model(): Compile a mixed-effects model spec without fitting
mm_control(): Control mixeff fitting behavior

Extractors (lme4-compatible)

The familiar lme4 accessor surface.

fixef() ranef() coef(<mm_lmm>) coef(<mm_glmm>) VarCorr() sigma(<mm_lmm>) sigma(<mm_glmm>) logLik(<mm_lmm>) logLik(<mm_glmm>) deviance(<mm_lmm>) deviance(<mm_glmm>) AIC(<mm_lmm>) AIC(<mm_glmm>) BIC(<mm_lmm>) BIC(<mm_glmm>) nobs(<mm_lmm>) nobs(<mm_glmm>) df.residual(<mm_lmm>) df.residual(<mm_glmm>) formula(<mm_lmm>) formula(<mm_glmm>) model.frame(<mm_lmm>) model.frame(<mm_glmm>) ngrps() extractAIC(<mm_lmm>) extractAIC(<mm_glmm>) terms(<mm_lmm>) terms(<mm_glmm>) as.data.frame(<mm_varcorr>) as.data.frame(<mm_ranef>) model.matrix(<mm_lmm>) model.matrix(<mm_glmm>) vcov(<mm_lmm>) vcov(<mm_glmm>): Extract components from a fitted mixeff LMM
getME(): Extract low-level model components
REMLcrit(): REML criterion of a linear mixed model
family(<mm_lmm>) family(<mm_glmm>) weights(<mm_lmm>) weights(<mm_glmm>): Family objects and prior weights for mixeff fits
is_singular(): Test whether a fit is singular or reduced-rank
predict(<mm_lmm>) fitted(<mm_lmm>) residuals(<mm_lmm>) fitted(<mm_glmm>) residuals(<mm_glmm>): Predict from a fitted mixeff LMM
predict(<mm_glmm>): Predict from a fitted mixeff GLMM
update(<mm_lmm>) update(<mm_glmm>): Update and re-fit a mixeff model
refit(): Refit a mixeff model with a new response
simulate(<mm_lmm>) simulate(<mm_glmm>): Simulate responses from a mixeff fit
rePCA(): PCA of the random-effects covariance (lme4's rePCA())
mm_lmlist(): Per-group linear (or generalized linear) model fits

Diagnostics and influence

Residual and random-effect plots, leverage, influence, R2 and ICC.

plot(<mm_lmm>) plot(<mm_glmm>) qqnorm(<mm_lmm>) qqnorm(<mm_glmm>): Diagnostic plots for mixeff fits
plot(<mm_ranef>) qqnorm(<mm_ranef>): Caterpillar and Q-Q plots of random effects
hatvalues(<mm_lmm>) hatvalues(<mm_glmm>) cooks.distance(<mm_lmm>) cooks.distance(<mm_glmm>) influence(<mm_lmm>) influence(<mm_glmm>) dfbeta(<mm_influence>) dfbetas(<mm_influence>) cooks.distance(<mm_influence>): Leverage, Cook's distance and deletion influence for mixeff fits
mm_variance_components() mm_r2() mm_icc(): Variance decomposition, Nakagawa R2 and ICC for mixeff fits

Inference

Contrasts, term tests, degrees of freedom, intervals, and bootstrap.

contrast(): Contrast fixed effects
test_effect(): Test a fixed-effect term
test_random_effect(): Test a random-effect variance component
anova(<mm_lmm>): Analysis of variance for a mixeff LMM
anova(<mm_glmm>): Analysis of deviance for GLMMs
drop1(<mm_lmm>): Drop one fixed-effect term at a time
drop1(<mm_glmm>): Drop one fixed-effect term at a time from a GLMM
confint(<mm_lmm>): Confidence intervals for a mixeff LMM
confint(<mm_glmm>): Confidence intervals for fixed effects of a mixeff GLMM
profile(<mm_lmm>): Profile a fitted linear mixed model
df_for_contrast(): Degrees of freedom for a contrast
estimability(): Assess contrast estimability
inference_options(): Inspect inference methods available for this fit
inference_table(): Fixed-effect inference table
bootstrap_control(): Fixed-effect bootstrap control
parametric_bootstrap(): Parametric bootstrap likelihood-ratio comparison
mm_bootmer(): Parametric bootstrap of any statistic of a fitted LMM

Marginal effects & emmeans

mm_grid() mm_predictions() mm_means() mm_comparisons(): Marginal grids, predictions, means, and comparisons
mm_lincomb(): Wald inference on a linear combination of fixed effects
recover_data.mm_lmm() emm_basis.mm_lmm() recover_data.mm_glmm() emm_basis.mm_glmm(): Optional emmeans support for mixeff models

Model comparison & reporting

compare(): Compare fitted mixeff models
compare_covariance(): Compare covariance parameterizations for current random terms
step(): Stepwise model selection
step(<mm_lmm>): Backward elimination of random and fixed effects (lmerTest's step())
mm_allfit(): Refit a model with every available optimizer
model_report() reporting_table(): Produce reporting tables for a fitted mixeff model

Audit-first verbs

Make the model’s design, reductions, and optimizer state explicit.

audit(): Audit a compiled model spec or fitted model
audit_design(): Deprecated alias for audit()
explain_model(): Explain the random-effects structure of a compiled model
changes(): Show requested, effective, and fitted model-state changes
diagnostics() fit_status(): Inspect mixeff diagnostics and fit status
parameterization(): Inspect covariance parameterization
random_options(): Inspect nearby random-effect spellings for one grouping factor
random_blocks(): Inspect random-effect blocks
roles(): Declare or inspect design roles
optimizer_certificate(): Inspect the optimizer certificate
verify_convergence(): Verify convergence of a fitted mixed model
reproducibility(): Inspect reproducibility metadata

broom / broom.mixed

mm_broom: Tidy, glance, and augment methods for mixeff fits

Persistence

as_json(): Serialize a mixeff spec or fit to JSON
revive(): Revive a serialized mixeff object
fit_handle_alive(): Test whether a mixeff fit has a live native handle

Formula & schema internals

mm_parse_formula(): Parse and canonicalize an lme4-style formula
mm_formula_manifest(): The wrapper's formula manifest
mm_json_negotiate(): Negotiate a JSON schema header against what mixeff supports
mm_json_known_schemas(): Closed list of schema/version pairs the wrapper understands

Package

mixeff mixeff-package: mixeff: Mixed-Effects Models via the 'mixeff-rs' Rust Crate