Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models [PDF]
Scientific Reports, 2017 Upon a drastic change in environmental illumination, zebrafish larvae display a rapid locomotor response. This response can be simultaneously tracked from larvae arranged in multi-well plates.
Yiwen Liu +10 more
doaj +2 more sources
Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study [PDF]
BMC Medical Research Methodology, 2018 Background Systematic reviews and meta-analyses of binary outcomes are widespread in all areas of application. The odds ratio, in particular, is by far the most popular effect measure.
Ilyas Bakbergenuly, Elena Kulinskaya
doaj +2 more sources
The R2D2 prior for generalized linear mixed models. [PDF]
Am StatIn Bayesian analysis, the selection of a prior distribution is typically done by considering each parameter in the model. While this can be convenient, in many scenarios it may be desirable to place a prior on a summary measure of the model instead.
Yanchenko E, Bondell HD, Reich BJ.
europepmc +4 more sources
MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package [PDF]
Journal of Statistical Software, 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.
Jarrod Had
doaj +1 more source
The Estimation of Item Response Models with the lmer Function from the lme4 Package in R [PDF]
Journal of Statistical Software, 2011 In this paper we elaborate on the potential of the lmer function from the lme4 package in R for item response (IRT) modeling. In line with the package, an IRT framework is described based on generalized linear mixed modeling. The aspects of the framework
Paul De Boeck +6 more
doaj +1 more source
Implementation of generalized estimating equations and mixed linear models in Python [PDF]
Yixue xinzhi zazhi, 2022 Objective Explore the implementation of generalized estimation equations (GEE) and mixed linear models (MLM) in longitudinal data analysis using Python software, and expand its application in statistical analysis.Methods GEE and MLM were constructed by ...
Kui-Zhuang JIAO +5 more
doaj +1 more source
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
doaj +1 more source
Bayesian Inference for Spatial Beta Generalized Linear Mixed Models [PDF]
Journal of Sciences, Islamic Republic of Iran, 2018 In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model
L. Kalhori Nadrabadi, M. Mohhamadzadeh
doaj +1 more source
Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation [PDF]
, 2011 Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates.
Groll, Andreas
core +5 more sources
Comparison of predictor approaches for longitudinal binary outcomes: application to anesthesiology data [PDF]
PeerJ, 2014 Longitudinal data with binary repeated responses are now widespread among clinical studies and standard statistical analysis methods have become inadequate in the answering of clinical hypotheses.
Anil Aktas Samur +2 more
doaj +2 more sources