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Open AccessJournal ArticleDOI

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.

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Endophyte traits relevant to stress tolerance, resource use and habitat of origin predict effects on host plants

TL;DR: Overall, survival was higher for plants grown with more stress-tolerant fungi, and aboveground biomass was enhanced by fungi from warmer and drier habitats, while plant growth and physiology were also dependent on fungal resource use indicators; however, specific predictors were dependent on water availability.
Journal ArticleDOI

Fixation durations in scene viewing: Modeling the effects of local image features, oculomotor parameters, and task

TL;DR: A LMM-based framework of analysis, applied to the control of fixation durations in scenes, suggests important constraints for models of scene perception and search, and for visual attention in general.
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Estimating aboveground net biomass change for tropical and subtropical forests : refinement of IPCC default rates using forest plot data

TL;DR: This study provides a rigorous and traceable refinement of the IPCC 2006 default rates in tropical and subtropical ecological zones, and identifies which areas require more research on ∆AGB.
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How much is enough? Effects of technical and biological replication on metabarcoding dietary analysis.

TL;DR: It is found that diet diversity estimates increased steadily with the number of pellets analysed per individual, with seven pellets required to detect ~80% of prey species, and most variation in diet composition was associated with differences among individual bats, followed by pellets per individual and PCRs per pellet.
References
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Journal Article

R: A language and environment for statistical computing.

R Core Team
- 01 Jan 2014 - 
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.
Book

Bayesian Data Analysis

TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Book

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

Data Analysis Using Regression and Multilevel/Hierarchical Models

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.