Package index • lme4

All functions

Arabidopsis: Arabidopsis clipping/fertilization data

Dyestuff Dyestuff2: Yield of dyestuff by batch

GHrule(): Univariate Gauss-Hermite quadrature rule

GQdk() GQN: Sparse Gaussian / Gauss-Hermite Quadrature grid

InstEval: University Lecture/Instructor Evaluations by Students at ETH

NelderMead(): Class "NelderMead" of Nelder-Mead optimizers and its Generator

Nelder_Mead(): Nelder-Mead Optimization of Parameters, Possibly (Box) Constrained

Pastes: Paste strength by batch and cask

Penicillin: Variation in penicillin testing

VarCorr(<merMod>) as.data.frame(<VarCorr.merMod>) print(<VarCorr.merMod>): Extract Variance and Correlation Components

VerbAgg: Verbal Aggression item responses

allFit(): Refit a fitted model with all available optimizers

bootMer(): Model-based (Semi-)Parametric Bootstrap for Mixed Models

cake: Breakage Angle of Chocolate Cakes

cbpp: Contagious bovine pleuropneumonia

checkConv(): Extended Convergence Checking

confint(<merMod>) confint(<thpr>): 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(<merMod>): Drop all possible single fixed-effect terms from a mixed effect model

dummy(): Dummy variables (experimental)

factorize(): Attempt to convert grouping variables to factors

fixef(<merMod>): Extract fixed-effects estimates

fortify.merMod() getData(<merMod>): add information to data based on a fitted model

getME(): Extract or Get Generalized Components from a Fitted Mixed Effects Model

glmFamily-class: Class "glmFamily" - a reference class for family

glmFamily(): Generator object for the glmFamily class

glmer(): Fitting Generalized Linear Mixed-Effects Models

glmer.nb(): Fitting Negative Binomial GLMMs

glmerLaplaceHandle(): Handle for glmerLaplace

golden(): Class "golden" and Generator for Golden Search Optimizer Class

grouseticks: Data on red grouse ticks from Elston et al. 2001

hatvalues(<merMod>): Diagonal elements of the hat matrix

influence(<merMod>) cooks.distance(<influence.merMod>) dfbeta(<influence.merMod>) dfbetas(<influence.merMod>): Influence Diagnostics for Mixed-Effects Models

isNested(): Is f1 nested within f2?

isREML() isLMM() isNLMM() isGLMM(): Check characteristics of models

isSingular(): Test Fitted Model for (Near) Singularity

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

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

anova(<merMod>) as.function(<merMod>) coef(<merMod>) deviance(<merMod>) REMLcrit() extractAIC(<merMod>) family(<merMod>) formula(<merMod>) fitted(<merMod>) logLik(<merMod>) nobs(<merMod>) ngrps(<merMod>) terms(<merMod>) model.frame(<merMod>) model.matrix(<merMod>) print(<merMod>) summary(<merMod>) print(<summary.merMod>) update(<merMod>) weights(<merMod>): 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

mkNewReTrms(): Make new random effect terms for prediction

mkRespMod(): Create an lmerResp, glmResp or nlsResp instance

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

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

plot(<merMod>) qqmath(<merMod>): Diagnostic Plots for 'merMod' Fits

xyplot(<thpr>) densityplot(<thpr>) splom(<thpr>): Mixed-Effects Profile Plots (Regular / Density / Pairs)

predict(<merMod>): Predictions from a model at new data values

profile(<merMod>) as.data.frame(<thpr>) log(<thpr>) logProf() varianceProf(): Profile method for merMod objects

mcmcsamp pvalues: Getting p-values for fitted models

ranef(<merMod>) dotplot(<ranef.mer>) qqmath(<ranef.mer>) as.data.frame(<ranef.mer>): Extract the modes of the random effects

rePCA(): PCA of random-effects covariance matrix

rePos-class: Class "rePos"

rePos(): Generator object for the rePos (random-effects positions) class

refit(): Refit a (merMod) Model with a Different Response

refitML(): Refit a Model by Maximum Likelihood Criterion

residuals(<merMod>) residuals(<lmResp>) residuals(<glmResp>): residuals of merMod objects

sigma(<merMod>): Extract Residual Standard Deviation 'Sigma'

simulate(<formula>): A simulate Method for formula objects that dispatches based on the Left-Hand Side

simulate(<merMod>) .simulateFun(): Simulate Responses From merMod Object

sleepstudy: Reaction times in a sleep deprivation study

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

mlist2vec() vec2mlist() vec2STlist() sdcor2cov() cov2sdcor() Vv_to_Cv() Sv_to_Cv() Cv_to_Vv() Cv_to_Sv(): Convert between representations of (co-)variance structures

vcov(<merMod>): Covariance matrix of estimated parameters