BLMM: Parallelised computing for big linear mixed models [PDF]
NeuroImage, 2022 Within neuroimaging large-scale, shared datasets are becoming increasingly commonplace, challenging existing tools both in terms of overall scale and complexity of the study designs.
Thomas Maullin-Sapey, Thomas E. Nichols
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Neighborhood-level heterogeneity in childhood morbidity through generalized linear mixed models [PDF]
Frontiers in Public HealthObjectiveChildhood morbidities are crucial for improving long-term public health outcomes. This study aimed to examine the existence of child-specific and regional variation in childhood morbidity based on the cross-cutting study of the Performance ...
Endeshaw A. Derso +8 more
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Federated generalized linear mixed models for collaborative genome-wide association studies [PDF]
iScience, 2023 Summary: Federated association testing is a powerful approach to conduct large-scale association studies where sites share intermediate statistics through a central server. There are, however, several standing challenges.
Wentao Li +3 more
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A reproducible, diagnostic-guided hybrid Laplace workflow for generalized linear mixed models [PDF]
MethodsXLaplace approximation is widely used for generalized linear mixed models because it is fast and compatible with automatic-differentiation software, yet its accuracy can deteriorate when clusters are weakly informative, outcomes are sparse, or conditional
Mohammad M. Hamasha
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Network analysis of longitudinal electronic health records using linear mixed models [PDF]
BioData MiningBackground The accelerating development of healthcare data stored in electronic health records (EHRs) has created novel opportunities for biomedical research.
Marina Vargas-Fernández +3 more
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partR2: partitioning R2 in generalized linear mixed models [PDF]
PeerJ, 2021 The coefficient of determination R2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by ...
Martin A. Stoffel +2 more
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Variational Bayesian Inference in High-Dimensional Linear Mixed Models
Mathematics, 2022 In high-dimensional regression models, the Bayesian lasso with the Gaussian spike and slab priors is widely adopted to select variables and estimate unknown parameters. However, it involves large matrix computations in a standard Gibbs sampler.
Jieyi Yi, Niansheng Tang
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Polygenic modeling with bayesian sparse linear mixed models. [PDF]
PLoS Genetics, 2013 Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies.
Xiang Zhou +2 more
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CytoGLMM: conditional differential analysis for flow and mass cytometry experiments
BMC Bioinformatics, 2021 Background Flow and mass cytometry are important modern immunology tools for measuring expression levels of multiple proteins on single cells. The goal is to better understand the mechanisms of responses on a single cell basis by studying differential ...
Christof Seiler +7 more
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Generalized linear mixed models can detect unimodal species-environment relationships [PDF]
PeerJ, 2013 Niche theory predicts that species occurrence and abundance show non-linear, unimodal relationships with respect to environmental gradients. Unimodal models, such as the Gaussian (logistic) model, are however more difficult to fit to data than linear ...
Tahira Jamil, Cajo J.F. ter Braak
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