Fitting Linear Mixed-Effects Models Using lme4
TLDR
In this article, a model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms, and the formula and data together determine a numerical representation of the model from which the profiled deviance or the profeatured REML criterion can be evaluated as a function of some of model parameters.Abstract:
Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.read more
Citations
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Journal ArticleDOI
Advanced Bayesian Multilevel Modeling with the R Package brms
TL;DR: Brms provides an intuitive and powerful formula syntax, which extends the well known formula syntax of lme4, which is introduced in detail and demonstrated its usefulness with four examples, each showing other relevant aspects of the syntax.
Journal ArticleDOI
The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded.
TL;DR: This paper generalizes the methods called for Poisson and binomial GLMMs to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data and can be used across disciplines and regardless of statistical environments.
Journal ArticleDOI
Spatial and temporal patterns of mass bleaching of corals in the Anthropocene.
Terry P. Hughes,Kristen G. Anderson,Sean R. Connolly,Scott F. Heron,Scott F. Heron,James T. Kerry,Janice M. Lough,Janice M. Lough,Andrew H. Baird,Julia K. Baum,Michael L. Berumen,Tom C. L. Bridge,Tom C. L. Bridge,Danielle C. Claar,C. Mark Eakin,James P. Gilmour,Nicholas A. J. Graham,Nicholas A. J. Graham,Hugo B. Harrison,Jean-Paul A. Hobbs,Andrew S. Hoey,Mia O. Hoogenboom,Ryan J. Lowe,Malcolm T. McCulloch,John M. Pandolfi,Morgan S. Pratchett,Verena Schoepf,Gergely Torda,Gergely Torda,Shaun K. Wilson +29 more
TL;DR: Coral reefs in the present day have less time than in earlier periods to recover from bleaching events, and Tropical reef systems are transitioning to a new era in which the interval between recurrent bouts of coral bleaching is too short for a full recovery of mature assemblages.
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A brief introduction to mixed effects modelling and multi-model inference in ecology.
Xavier A. Harrison,Lynda Donaldson,Lynda Donaldson,Maria Eugenia Correa-Cano,Julian C. Evans,Julian C. Evans,David N. Fisher,David N. Fisher,Cecily E. D. Goodwin,Beth S. Robinson,David J. Hodgson,Richard Inger +11 more
TL;DR: This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.
Journal ArticleDOI
TGFβ drives immune evasion in genetically reconstituted colon cancer metastasis
Daniele V.F. Tauriello,Sergio Palomo-Ponce,Diana Stork,Antonio Berenguer-Llergo,Jordi Badia-Ramentol,Mar Iglesias,Marta Sevillano,Sales Ibiza,Adrià Cañellas,Xavier Hernando-Momblona,Daniel Byrom,Joan A. Matarin,Alexandre Calon,Elisa I. Rivas,Angel R. Nebreda,Antoni Riera,Camille Stephan-Otto Attolini,Eduard Batlle +17 more
TL;DR: Increased TGFβ in the tumour microenvironment represents a primary mechanism of immune evasion that promotes T-cell exclusion and blocks acquisition of the TH1-effector phenotype, and immunotherapies directed against TGF β signalling may have broad applications in treating patients with advanced colorectal cancer.
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TL;DR: Linear Mixed-Effects and Nonlinear Mixed-effects (NLME) models have been studied in the literature as mentioned in this paper, where the structure of grouped data has been used for fitting LME models.
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