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lme4: Mixed-effects models in R.

• Douglas Bates: Author.

• Martin Maechler: Author.

• Ben Bolker: Author, maintainer.

• Steven Walker: Author.

• Rune Haubo Bojesen Christensen: Contributor.

• Henrik Singmann: Contributor.

• Bin Dai: Contributor.

• Fabian Scheipl: Contributor.

• Gabor Grothendieck: Contributor.

• Peter Green: Contributor.

• John Fox: Contributor.

• Alexander Bauer: Contributor.

• Pavel N. Krivitsky: Contributor, copyright holder. shared copyright on simulate.formula

• Emi Tanaka: Contributor.

Alessio Pignatelli (Did not write the package, authors are listed above)

Implementation of generalized linear mixed models (GLMMs) and nonlinear mixed models (NLMMs)

• allFit() - Refit a fitted model with all available optimizers
• Arabidopsis - Arabidopsis clipping/fertilization data
• bootMer() - Model-based (Semi-)Parametric Bootstrap for Mixed Models
• cake - Breakage Angle of Chocolate Cakes
• cbpp - Contagious bovine pleuropneumonia
• checkConv() - Extended Convergence Checking
• confint() confint() - Compute Confidence Intervals for Parameters of a [ng]lmer Fit
• convergence Assessing Convergence for Fitted Models
• devcomp() - Extract the deviance component list
• devfun2() - Deviance Function in Terms of Standard Deviations/Correlations
• drop1() - Drop all possible single fixed-effect terms from a mixed effect model
• dummy() - Dummy variables (experimental)
• Dyestuff Dyestuff2 - Yield of dyestuff by batch
• expandDoubleVerts() - Expand terms with '||' notation into separate '|' terms
• factorize() - Attempt to convert grouping variables to factors
• findbars() - Determine random-effects expressions from a formula
• fixef() - Extract fixed-effects estimates
• fortify.merMod() getData() - add information to data based on a fitted model
• getME() - Extract or Get Generalized Components from a Fitted Mixed Effects Model
• GHrule() - Univariate Gauss-Hermite quadrature rule
• glmer.nb() - Fitting Negative Binomial GLMMs
• glmer() - Fitting Generalized Linear Mixed-Effects Models
• glmerLaplaceHandle() - Handle for glmerLaplace
• glmFamily-class - Class "glmFamily" - a reference class for family
• glmFamily() - Generator object for the glmFamily class
• golden() - Class "golden" and Generator for Golden Search Optimizer Class
• GQdk() - GQN Sparse Gaussian / Gauss-Hermite Quadrature grid
• grouseticks - Data on red grouse ticks from Elston et al. 2001
• hatvalues() Diagonal elements of the hat matrix
• influence() cooks.distance(<influence.merMod>) dfbeta(<influence.merMod>) dfbetas(<influence.merMod>) - Influence Diagnostics for Mixed-Effects Models
• InstEval - University Lecture/Instructor Evaluations by Students at ETH
• isNested() - Is f1 nested within f2?
• isREML() isLMM() isNLMM() isGLMM() - Check characteristics of models
• isSingular() - Test Fitted Model for (Near) Singularity
• lme4 lme4-package - Linear, generalized linear, and nonlinear mixed models
• lme4_testlevel() - Detect testing level for lme4 examples and tests
• lmer() - Fit Linear Mixed-Effects Models
• lmerControl() glmerControl() nlmerControl() .makeCC() - Control of Mixed Model Fitting
• lmList() - Fit List of lm or glm Objects with a Common Model
• lmList4-class show,lmList4-method - Class "lmList4" of 'lm' Objects on Common Model
• glmResp-class lmerResp-class lmResp-class nlsResp-class - Reference Classes for Response Modules, "(lm|glm|nls|lmer)Resp"
• lmResp() - Generator objects for the response classes
• anova() as.function() coef() deviance() REMLcrit() extractAIC() family() formula() fitted() logLik() nobs() ngrps() terms() vcov() model.frame() model.matrix() print() summary() print(<summary.merMod>) update() weights() - Class "merMod" of Fitted Mixed-Effect Models
• merPredD-class - Class "merPredD" - a Dense Predictor Reference Class
• merPredD() - Generator object for the merPredD class
• mkMerMod() - Create a 'merMod' Object
• mkRespMod() - Create an lmerResp, glmResp or nlsResp instance
• mkReTrms() mkNewReTrms() - Make Random Effect Terms: Create Z, Lambda, Lind, etc.
• mkParsTemplate() mkDataTemplate() - Make templates suitable for guiding mixed model simulations
• mkVarCorr() - Make Variance and Correlation Matrices from theta
• lFormula() mkLmerDevfun() optimizeLmer() glFormula() mkGlmerDevfun() optimizeGlmer() updateGlmerDevfun() - Modular Functions for Mixed Model Fits
• namedList() - Self-naming list function
• NelderMead() - Class "NelderMead" of Nelder-Mead optimizers and its Generator
• Nelder_Mead() - Nelder-Mead Optimization of Parameters, Possibly (Box) Constrained
• ngrps() - Number of Levels of a Factor or a "merMod" Model
• nlformula() - Manipulate a Nonlinear Model Formula
• nlmer() - Fitting Nonlinear Mixed-Effects Models
• nloptwrap() nlminbwrap() - Wrappers for additional optimizers
• nobars() - Omit terms separated by vertical bars in a formula
• Pastes - Paste strength by batch and cask
• Penicillin - Variation in penicillin testing
• plot() qqmath() - Diagnostic Plots for 'merMod' Fits
• xyplot() densityplot() splom() - Mixed-Effects Profile Plots (Regular / Density / Pairs)
• predict() - Predictions from a model at new data values
• profile() as.data.frame() log() logProf() varianceProf() - Profile method for merMod objects
• mcmcsamp pvalues - Getting p-values for fitted models
• ranef() dotplot(<ranef.mer>) qqmath(<ranef.mer>) as.data.frame(<ranef.mer>) - Extract the modes of the random effects
• refit() - Refit a (merMod) Model with a Different Response
• refitML() - Refit a Model by Maximum Likelihood Criterion
• rePCA() - PCA of random-effects covariance matrix
• rePos-class - Class "rePos"
• rePos() - Generator object for the rePos (random-effects positions) class
• residuals() residuals() residuals() - residuals of merMod objects
• sigma() - Extract Residual Standard Deviation 'Sigma'
• simulate() - A simulate Method for formula objects that dispatches based on the Left-Hand Side
• simulate() .simulateFun() - Simulate Responses From merMod Object
• sleepstudy - Reaction times in a sleep deprivation study
• subbars() "Sub[stitute] Bars"
• troubleshooting - Troubleshooting
• llikAIC() methTitle() .prt.methTit() .prt.family() .prt.resids() .prt.call() .prt.aictab() .prt.grps() .prt.warn() .prt.VC() formatVC() - Print and Summary Method Utilities for Mixed Effects
• VarCorr() as.data.frame(<VarCorr.merMod>) print(<VarCorr.merMod>) - Extract Variance and Correlation Components
• mlist2vec() vec2mlist() vec2STlist() sdcor2cov() cov2sdcor() Vv_to_Cv() Sv_to_Cv() Cv_to_Vv() Cv_to_Sv() - Convert between representations of (co-)variance structures
• VerbAgg - Verbal Aggression item responses

# Install and load the lme4 package
# install.packages("lme4")
library(lme4)

# Generate some example data
set.seed(123)
data <- data.frame(
  Group = rep(c("A", "B"), each = 5),
  Score = rnorm(10),
  Subject = factor(rep(1:5, 2))
)

# Fit a linear mixed-effects model
model <- lmer(Score ~ Group + (1 | Subject), data = data)

# Print the model summary
summary(model)

• See the NEWS file

• [email protected] for questions about lme4 usage and more general mixed model questions; please read the info page, and subscribe, before posting ... (note that the mailing list does not support images or large/non-text attachments)
• https://github.com/lme4/lme4/issues for bug, infelicity, and wishlist reporting
• The lme4 tag on StackOverflow for programming-related or the lme4-nlme tag on CrossValidated for statistics-related questions
• maintainer e-mail only for urgent/private communications

If you choose to support lme4 development financially, you can contribute to a fund at McMaster University (home institution of one of the developers) here. The form will say that you are donating to the "Global Coding Fund"; this fund is available for use by the developers, under McMaster's research spending rules. We plan to use the funds, as available, to pay students to do maintenance and development work. There is no way to earmark funds or set up a bounty to direct funding toward particular features, but you can e-mail the maintainers and suggest priorities for your donation.

• Efficient for large data sets, using algorithms from the Eigen linear algebra package via the RcppEigen interface layer.
• Allows arbitrarily many nested and crossed random effects.
• Fits generalized linear mixed models (GLMMs) and nonlinear mixed models (NLMMs) via Laplace approximation or adaptive Gauss-Hermite quadrature; GLMMs allow user-defined families and link functions.
• Incorporates likelihood profiling and parametric bootstrapping.

• From CRAN (stable release 1.0.+)
• Development version from Github:

library("devtools"); install_github("lme4/lme4",dependencies=TRUE)

(This requires devtools >= 1.6.1, and installs the "master" (development) branch.) This approach builds the package from source, i.e. make and compilers must be installed on your system -- see the R FAQ for your operating system; you may also need to install dependencies manually. Specify build_vignettes=FALSE if you have trouble because your system is missing some of the LaTeX/texi2dvi tools. * Development binaries from lme4 r-forge repository:

install.packages("lme4",
   repos=c("http://lme4.r-forge.r-project.org/repos",
          getOption("repos")[["CRAN"]]))

(these source and binary versions are updated manually, so may be out of date; if you believe they are, please contact the maintainers).

It is possible to install (but not easily to check) lme4 at least as recently as 1.1-7.

• make sure you have exactly these package versions: Rcpp 0.10.5, RcppEigen 3.2.0.2
• for installation, use --no-inst; this is necessary in order to prevent R from getting hung up by the knitr-based vignettes
• running R CMD check is difficult, but possible if you hand-copy the contents of the inst directory into the installed package directory ...

• lme4.0 is a maintained version of lme4 back compatible to CRAN versions of lme4 0.99xy, mainly for the purpose of reproducible research and data analysis which was done with 0.99xy versions of lme4.
• there have been some reports of problems with lme4.0 on R version 3.1; if someone has a specific reproducible example they'd like to donate, please contact the maintainers.
• Notably, lme4.0 features getME(<mod>, "..") which is compatible (as much as sensibly possible) with the current lme4's version of getME().
• You can use the convert_old_lme4() function to take a fitted object created with lme4 <1.0 and convert it for use with lme4.0.
• It currently resides on R-forge, and you should be able to install it with

install.packages("lme4.0",
                 repos=c("http://lme4.r-forge.r-project.org/repos",
                         getOption("repos")[["CRAN"]]))

(if the binary versions are out of date or unavailable for your system, please contact the maintainers).