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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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Countries with Higher Levels of Gender Equality Show Larger National Sex Differences in Mathematics Anxiety and Relatively Lower Parental Mathematics Valuation for Girls

TL;DR: It is shown that economically developed and more gender equal countries have a lower overall level of mathematics anxiety, and yet a larger national sex difference in mathematics anxiety relative to less developed countries, and the gender stratification model fails to account for these national patterns.
Journal ArticleDOI

Antibiotic-producing symbionts dynamically transition between plant pathogenicity and insect-defensive mutualism

TL;DR: Evidence supporting a dynamic transition from plant pathogenicity to insect-defensive mutualism in symbiotic Burkholderia gladioli bacteria is presented, providing a paradigm for the transition between pathogenic and mutualistic lifestyles and shed light on the evolution and chemical ecology of this defensive mutualism.
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Prevalence and Family-Related Factors Associated With Suicidal Ideation, Suicide Attempts, and Self-injury in Children Aged 9 to 10 Years

TL;DR: This study demonstrates the association of family factors, including high family conflict and low parental monitoring, with suicidality and self-injury in children, as well as the overall prevalence of suicidal ideation, suicide attempts, and nonsuicidal self-Injury among preadolescent children.
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Longitudinal Study of Automatic Face Recognition

TL;DR: Longitudinal analysis of two mugshot databases shows that despite decreasing genuine scores, 99% of subjects can still be recognized at 0.01% FAR up to approximately 6 years elapsed time, and that age, sex, race, and race only marginally influence these trends.
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.