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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Data from: Mass-flowering crops dilute pollinator abundance in agricultural landscapes across Europe
Andrea Holzschuh,Matteo Dainese,Juan P. González-Varo,Verena Riedinger,Sonja Mudri-Stojnic,Maj Rundlöf,Jeroen Scheper,Jennifer B. Wickens,Victoria J. Wickens,Riccardo Bommarco,David Kleijn,Simon G. Potts,Stuart P. M. Roberts,Henrik G. Smith,Montserrat Vilà,Ante Vujić,Ingolf Steffan-Dewenter +16 more
TL;DR: In this paper, the authors assessed how landscape-scale cover of MFCs affected pollinator densities in 408 MFC fields and adjacent semi-natural habitats (SNHs).
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Differential soil fungus accumulation and density dependence of trees in a subtropical forest
TL;DR: It is shown that mycorrhizal type mediates tree neighborhood interactions at the community level, and much of the interspecific variation in CNDD is explained by how tree species differ in their fungal density accumulation rates as they grow.
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Big GABA: Edited MR spectroscopy at 24 research sites
Mark Mikkelsen,Mark Mikkelsen,Peter B. Barker,Peter B. Barker,Pallab K. Bhattacharyya,Pallab K. Bhattacharyya,Maiken K. Brix,Pieter F. Buur,Kim M. Cecil,Kimberly L. Chan,Kimberly L. Chan,David Yen Ting Chen,Alexander R. Craven,Koen Cuypers,Koen Cuypers,Michael Dacko,Niall W. Duncan,Ulrike Dydak,David A. Edmondson,Gabriele Ende,Lars Ersland,Fei Gao,Ian Greenhouse,Ashley D. Harris,Naying He,Stefanie Heba,Nigel Hoggard,Tun Wei Hsu,Jacobus F.A. Jansen,Alayar Kangarlu,Thomas Lange,R. Marc Lebel,Yan Li,Chien Yuan E. Lin,Jy Kang Liou,Jiing Feng Lirng,Feng Liu,Ruoyun Ma,Celine Maes,Marta Moreno-Ortega,Scott O. Murray,Sean Noah,Ralph Noeske,Michael D. Noseworthy,Georg Oeltzschner,Georg Oeltzschner,James J. Prisciandaro,Nicolaas A.J. Puts,Nicolaas A.J. Puts,Timothy P.L. Roberts,Markus Sack,Napapon Sailasuta,Muhammad G. Saleh,Muhammad G. Saleh,Michael-Paul Schallmo,Nicholas Simard,Stephan P. Swinnen,Martin Tegenthoff,Peter Truong,Guangbin Wang,Iain D. Wilkinson,Hans Jörg Wittsack,Hongmin Xu,Fuhua Yan,Chencheng Zhang,Vadim Zipunnikov,Helge J. Zöllner,Richard A.E. Edden,Richard A.E. Edden +68 more
TL;DR: The findings show that GABA+ measurements exhibit strong agreement when implemented with a standard protocol, and multi‐site studies using GABA editing are feasible using a standardized protocol.
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Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19.
Can Liu,Can Liu,Andrew J. Martins,William W. Lau,Nicholas Rachmaninoff,Nicholas Rachmaninoff,Jinguo Chen,Luisa Imberti,Darius Mostaghimi,Danielle Fink,Peter D. Burbelo,Kerry Dobbs,Ottavia M. Delmonte,Neha Bansal,Laura Failla,Alessandra Sottini,Eugenia Quiros-Roldan,Kyu Lee Han,Brian Sellers,Foo Cheung,Rachel Sparks,Tae Wook Chun,Susan Moir,Michail S. Lionakis,Michael S. Abers,Richard Apps,Marita Bosticardo,Pedro Milanez-Almeida,Matthew P. Mulé,Elana Shaw,Yu Zhang,Francesco Castelli,Maria Lorenza Muiesan,Gabriele Tomasoni,Francesco Scolari,Alessandra Tucci,Camillo Rossi,Helen C. Su,Douglas B. Kuhns,Jeffrey I. Cohen,Luigi D. Notarangelo,John S. Tsang +41 more
TL;DR: In this article, the authors longitudinally assessed circulating proteins as well as 188 surface protein markers, transcriptome, and T-cell receptor sequence simultaneously in single peripheral immune cells from COVID-19 patients.
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Globally, functional traits are weak predictors of juvenile tree growth, and we do not know why
C.E. Thimothy Paine,Lucy Amissah,Lucy Amissah,Harald Auge,Christopher Baraloto,Christopher Baraloto,Martin Baruffol,Nils Bourland,Helge Bruelheide,Kasso Daïnou,Roland C. de Gouvenain,Jean-Louis Doucet,Susan Doust,Paul V. A. Fine,Claire Fortunel,Claire Fortunel,Josephine Haase,Josephine Haase,Karen D. Holl,Hervé Jactel,Xuefei Li,Kaoru Kitajima,Kaoru Kitajima,Kaoru Kitajima,Julia Koricheva,Cristina Martínez-Garza,Christian Messier,Alain Paquette,Christopher D. Philipson,Daniel Piotto,Lourens Poorter,Juan M. Posada,Catherine Potvin,Catherine Potvin,Kalle Rainio,Sabrina E. Russo,Mariacarmen Ruiz-Jaen,Michael Scherer-Lorenzen,Campbell O. Webb,S. Joseph Wright,Rakan A. Zahawi,Andy Hector +41 more
TL;DR: It is concluded that the most widely studied functional traits in plant ecology were poor predictors of tree growth over large scales and may be unsuitable for predicting growth of trees over broad scales.
References
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TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
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Mixed-Effects Models in S and S-PLUS
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.
Book
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TL;DR: Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models.