Generalized linear mixed-effects models

Results 61 to 70 of about 490,993 (142)

How to Interpret Statistical Models Using marginaleffects for R and Python

Journal of Statistical Software
The parameters of a statistical model can sometimes be difficult to interpret substantively, especially when that model includes nonlinear components, interactions, or transformations.
Vincent Arel-Bundock, Noah Greifer, Andrew Heiss +2 more
doaj +1 more source

Genetic heterogeneity of residual variance - estimation of variance components using double hierarchical generalized linear models

Genetics Selection Evolution, 2010
Background The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals.
Fikse Freddy +4 more
doaj +1 more source

Multivariate Generalized Linear Mixed-Effects Models for the Analysis of Clinical Trial-Based Cost-Effectiveness Data. [PDF]

Med Decis Making, 2021
Achana F +6 more
europepmc +1 more source

A Tutorial on Analyzing Ecological Momentary Assessment Data in Psychological Research With Bayesian (Generalized) Mixed-Effects Models

Advances in Methods and Practices in Psychological Science
In this tutorial, we introduce the reader to analyzing ecological momentary assessment (EMA) data as applied in psychological sciences with the use of Bayesian (generalized) linear mixed-effects models.
Jonas Dora +4 more
doaj +1 more source

81 Fixing or mixing? Improving small sample size longitudinal microbiome models

Journal of Clinical and Translational Science
Objectives/Goals: Longitudinal studies capture dynamic host–environment interactions crucial to microbiome research. Commonly used mixed-effects models can struggle with small clinical samples, like many endometrial cancer studies.
Raunak Vijayakar, Jun Chen, Marina Walther-Antonio +2 more
doaj +1 more source

SNP_NLMM: A SAS Macro to Implement a Flexible Random Effects Density for Generalized Linear and Nonlinear Mixed Models

Journal of Statistical Software, 2014
Generalized linear and nonlinear mixed models (GLMMs and NLMMs) are commonly used to represent non-Gaussian or nonlinear longitudinal or clustered data. A common assumption is that the random effects are Gaussian.
David Vock, Marie Davidian, Anastasios Tsiatis +2 more
doaj +1 more source

Advocating better habitat use and selection models in bird ecology [PDF]

, 2018
Studies on habitat use and habitat selection represent a basic aspect of bird ecology, due to its importance in natural history, distribution, response to environmental changes, management and conservation.
Palacio, Facundo Xavier
core

Exploring Factors Associated with Adolescent Tuberculosis in India: Evidence from the National Family Health Survey (2019–21)

Diseases
Background: Tuberculosis (TB) in adolescents is distinct from both childhood and adult TB, particularly in terms of risk factors; however, national-level data assessing these factors in adolescents remain limited despite growing attention to the issue ...
Ratnakar Singh +4 more
doaj +1 more source

On displacement based non-local models for non-linear vibrations of thin nano plates

MATEC Web of Conferences, 2018
This paper addresses the formulation of displacement based, non-linear, plate models adopting Eringen's non-local elasticity, to study the modes of vibration of thin, nano plates.
Chuaqui Tomás R. C., Ribeiro Pedro
doaj +1 more source

Bayesian predictive modelling to ascertain factors affecting cattle milk production in Tanzania: Evidence from the national panel surveys 2012 – 2021

Veterinary and Animal Science
This study aimed to evaluate and compare Bayesian predictive models to identify and quantify the key household inputs affecting cattle milk production in Tanzania.
Zainabu Bonza, Rosalia Katapa, Amina Msengwa +2 more
doaj +1 more source

random effects
generalized linear mixed models
linear mixed models