We review, examine the performance, and discuss the relative strengths and weaknesses of various R functions for the estimation of generalized linear mixed-effects models (GLMMs) for binary outcomes. The R functions reviewed include glmer in the package lme4, hglm2 in the package hglm, ...
This function has been completely rewritten and included in the piecewiseSEM package assem.model.fits. See updates here:https://github.com/jslefche/piecewiseSEM/ Description: Implementation of Schielzeth and Nakagawa's R2 for generalized linear mixed effects models in R. This function improves on ...
Formulas include a constant (intercept) term by default. To exclude a constant term from the model, include–1in the formula. For generalized linear mixed-effects models, the formula specification is of the form'y ~ fixed + (random1|grouping1) + ... + (randomR|groupingR)', wherefixedan...
As a method to ascertain person and item effects in psycholinguistics, a generalized linear mixed effect model (GLMM) with crossed random effects has met limitations in handing serial dependence across persons and items. This paper presents an autoregressive GLMM with crossed random effects that accoun...
Information criteria, such as Akaike Information Criterion (AIC), are usually presented as model comparison tools for mixed-effects models. The presentation of variance explained' (R2) as a relevant summarizing statistic of mixed-effects models, however, is rare, even though R2 is routinely ...
Journal of Modern Applied StatisticalMethodsVolume 6 | Issue 2 Article 2511-1-2007Generalized Linear Mixed-Effects Models for theAnalysis of Odor Detection DataSandra HallUniversity of Kansas Medical CenterMatthew S. MayoUniversity of Kansas Medical Center, mmayo@kumc.eduXu-Feng NiuFlorida State ...
This paper focuses on model selection in generalized linear mixed models using an information criterion approach. In these models in general, the response marginal distribution cannot be analytically derived. Thus, for parameter estimation, two approximations are revisited both leading to iterative model ...
The generalized linear mixed-effects model (GLMM) is a popular paradigm to extend models for cross-sectional data to a longitudinal setting. When applied t... Z Hui,N Lu,C Feng,... - 《Statistics in Medicine》 被引量: 109发表: 2011年 Coefficient of determination R2 and intra-class corre...
We discuss a Bayesian hierarchical generalized linear mixed-effects model with a finite-support random-effects distribution and show how Gibbs sampling can be used for estimating the posterior distribution of the parameters and for clustering on the basis of longitudinal data. When directly sampling fro...
500 samples are drawn with the model specification: y = (intercept*f1+pred2*f2+pred3*f3)+(intercept*ri+pred2*rs) where pred2 and pred3 are predictors distributed N(0,1) f1..f3 are fixed effects, f1=-1, f2=1.5, f3=0.5