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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Genome-Wide Association and Prediction Reveals Genetic Architecture of Cassava Mosaic Disease Resistance and Prospects for Rapid Genetic Improvement
Marnin D. Wolfe,Ismail Y. Rabbi,Chiedozie Egesi,Martha T. Hamblin,Robert Kawuki,Peter Kulakow,Roberto Lozano,Dunia Pino Del Carpio,Punna Ramu,Jean-Luc Jannink +9 more
TL;DR: Cassava mosaic disease resistance has a narrow genetic basis in breeding germplasm and evidence suggests two possibly epistatic loci and/or multiple resistance alleles exist at the major QTL.
Journal ArticleDOI
Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package
TL;DR: The R package CARBayesST is presented, which is the first dedicated software package for spatio-temporal areal unit modeling with conditional autoregressive priors, and allows to fit a range of models focused on different aspects of spacetime modeling.
Journal ArticleDOI
Origin and function of stomata in the moss Physcomitrella patens
Caspar Chater,Robert S. Caine,Marta Tomek,Simon Wallace,Yasuko Kamisugi,Andrew C. Cuming,Daniel Lang,Cora A. MacAlister,Stuart A. Casson,Dominique C. Bergmann,Eva L. Decker,Wolfgang Frank,Julie E. Gray,Andrew J. Fleming,Ralf Reski,David J. Beerling +15 more
TL;DR: It is shown that genes encoding the two basic helix–loop–helix proteins PpSMF1 (SPEECH, MUTE and FAMA-like) and PpSCREAM1 (SCRM1) in the moss Physcomitrella patens are orthologous to transcriptional regulators of stomatal development in the flowering plant Arabidopsis thaliana and essential for stomata formation in moss.
Journal ArticleDOI
Virological factors that increase the transmissibility of emerging human viruses
TL;DR: It is determined that viruses with low host mortality, that establish long-term chronic infections, and that are nonsegmented, nonenveloped, and, most importantly, not transmitted by vectors were more likely to be transmissible among humans.
Journal ArticleDOI
The fixed versus random effects debate and how it relates to centering in multilevel modeling.
Ellen L. Hamaker,Bengt Muthén +1 more
TL;DR: This work focuses on 2 concerns, that is: (a) the concern about random effects versus fixed effects, which is central in the (micro)econometrics/sociology literature; and (b) the concerns about grand mean versus group (or person) mean centering, which are central inThe multilevel literature associated with disciplines like psychology and educational sciences.
References
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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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