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Linear Mixed Models: Gum and Beyond

Measurement Science Review, 2014
In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying random effects ANOVA models.
Arendacká Barbora, Täubner Angelika, Eichstädt Sascha, Bruns Thomas, Elster Clemens   +4 more
doaj   +1 more source

Modified BIC Criterion for Model Selection in Linear Mixed Models

Mathematics, 2023
Linear mixed-effects models are widely used in applications to analyze clustered, hierarchical, and longitudinal data. Model selection in linear mixed models is more challenging than that of linear models as the parameter vector in a linear mixed model ...
Hang Lai, Xin Gao
doaj   +1 more source

Fast and flexible linear mixed models for genome-wide genetics. [PDF]

PLoS Genetics, 2019
Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits.
Daniel E Runcie, Lorin Crawford
doaj   +1 more source

lmerTest Package: Tests in Linear Mixed Effects Models

Journal of Statistical Software, 2017
One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer?
Alexandra Kuznetsova, Per B. Brockhoff, Rune H. B. Christensen   +2 more
doaj   +1 more source

Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data [PDF]

, 2015
We propose an estimation approach to analyse correlated functional data which are observed on unequal grids or even sparsely. The model we use is a functional linear mixed model, a functional analogue of the linear mixed model.
Cederbaum, Jona, Greven, Sonja, Hoole, Phil, Pouplier, Marianne   +3 more
core   +2 more sources

Accessible analysis of longitudinal data with linear mixed effects models

Disease Models & Mechanisms, 2022
Longitudinal studies are commonly used to examine possible causal factors associated with human health and disease. However, the statistical models, such as two-way ANOVA, often applied in these studies do not appropriately model the experimental design,
Jessica I. Murphy, Nicholas E. Weaver, Audrey E. Hendricks   +2 more
doaj   +1 more source

Violation of the Sphericity Assumption and Its Effect on Type-I Error Rates in Repeated Measures ANOVA and Multi-Level Linear Models (MLM)

Frontiers in Psychology, 2017
We investigated the effects of violations of the sphericity assumption on Type I error rates for different methodical approaches of repeated measures analysis using a simulation approach.
Nicolas Haverkamp, André Beauducel
doaj   +1 more source

Simple reparameterization to improve convergence in linear mixed models

Acta Agriculturae Slovenica, 2010
Slow convergence and mixing are one of the main problems of Markov chain Monte Carlo (McMC) algorithms applied to mixed models in animal breeding. Poor convergence is to a large extent caused by high posterior correlation between variance components and ...
Gregor GORJANC, Tina FLISAR, Jose Carlos MARTÍNEZ-ÁVILA, Luis Alberto GARCÍA-CORTÉS   +3 more
doaj   +1 more source

Using linear mixed models to analyze learning processes within sessions improves detection of treatment effects: An exemplary study of chronometric mental rotation

Methods in Psychology, 2022
Practice effects occur for many cognitive tasks. They are observed not only between repeated tests, but also within sessions. They can confound the detection of treatment effects, even when compared with control groups.
Leonardo Jost, Petra Jansen
doaj   +1 more source

A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence [PDF]

, 2015
A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that it is superior to the standard generalized linear mixed model in this context.
Aas K, Aristidis K Nikoloulopoulos, Brenner H, Chu H, Chu H, Demidenko E, Genest C, Genest C, Genest C, Hamza TH, Hoyer A, Hua L, Hult H, Joe H, Joe H, Joe H, Joe H, Joe H, Karageorgopoulos DE, Kurowicka D, Kuss O, Leeflang MMG, Leeflang MMG, Ma X, Mavridis D, McNeil AJ, Nash JC, Nelsen RB, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Panagiotelis A, Paul M, Serfling RJ, Shen C, Stroud AH, Vuong QH, Ye Y   +41 more
core   +2 more sources