Results 31 to 40 of about 3,822,020 (321)
MCMC methods for multi-response generalized linear mixed models
, 2010 Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form.
J. Hadfield
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bioRxiv, 2016 The coefficient of determination R2 quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest.
S Nakagawa, P. C. Johnson, H. Schielzeth
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Meta-analysis of Proportions Using Generalized Linear Mixed Models
Epidemiology, 2020 Supplemental Digital Content is available in the text. Epidemiologic research often involves meta-analyses of proportions. Conventional two-step methods first transform each study’s proportion and subsequently perform a meta-analysis on the transformed ...
Lifeng Lin, H. Chu
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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 +2 more
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Robustness of linear mixed‐effects models to violations of distributional assumptions
Methods in Ecology and Evolution, 2020 Linear mixed‐effects models are powerful tools for analysing complex datasets with repeated or clustered observations, a common data structure in ecology and evolution. Mixed‐effects models involve complex fitting procedures and make several assumptions,
H. Schielzeth +9 more
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Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R
Behavior Research Methods, 2020 Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly rejects the null hypothesis.
Levi Kumle, M. Võ, Dejan Draschkow
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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
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How to capitalize on a priori contrasts in linear (mixed) models: A tutorial [PDF]
Journal of Memory and Language, 2018 Factorial experiments in research on memory, language, and in other areas are often analyzed using analysis of variance (ANOVA). However, for effects with more than one numerator degrees of freedom, e.g., for experimental factors with more than two ...
D. Schad +3 more
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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 +3 more
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Simultaneous Inference in General Parametric Models [PDF]
, 2008 Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level ...
Bates +29 more
core +3 more sources