Results 261 to 270 of about 3,822,020 (321)
Some of the next articles are maybe not open access.
A coefficient of determination (R2) for generalized linear mixed models
Biometrical journal. Biometrische Zeitschrift, 2019Extensions of linear models are very commonly used in the analysis of biological data. Whereas goodness of fit measures such as the coefficient of determination (R2) or the adjusted R2 are well established for linear models, it is not obvious how such ...
H. Piepho
semanticscholar +1 more source
Model comparison of generalized linear mixed models
Statistics in Medicine, 2005AbstractGeneralized linear mixed models (GLMMs) have been widely appreciated in biological and medical research. Maximum likelihood estimation has received a great deal of attention. Comparatively, not much has been done on model comparison or hypotheses testing.
Xin-Yuan, Song, Sik-Yum, Lee
openaire +2 more sources
2009
In the early 1950s, C.R. Henderson developed mixed model estimation, something he began in the 1940s with his Ph.D. thesis. He wanted to analyze data for a linear model with fixed environmental and random genetic factors in the breeding of swine (Van Vleck, 1998).
openaire +1 more source
In the early 1950s, C.R. Henderson developed mixed model estimation, something he began in the 1940s with his Ph.D. thesis. He wanted to analyze data for a linear model with fixed environmental and random genetic factors in the breeding of swine (Van Vleck, 1998).
openaire +1 more source
2013
The simplest form of the linear mixed model is the random-effects model, which represents data using the regression equation: $$\displaystyle{ \mathbf{y}_{i} =\boldsymbol{\alpha } +\mathbf{b}_{i} +\boldsymbol{\epsilon } _{i} (1 \leq i \leq m), }$$ where \(\boldsymbol{\alpha }\), y i , b i , and \(\boldsymbol{\epsilon }_{i}\) are column matrices ...
openaire +1 more source
The simplest form of the linear mixed model is the random-effects model, which represents data using the regression equation: $$\displaystyle{ \mathbf{y}_{i} =\boldsymbol{\alpha } +\mathbf{b}_{i} +\boldsymbol{\epsilon } _{i} (1 \leq i \leq m), }$$ where \(\boldsymbol{\alpha }\), y i , b i , and \(\boldsymbol{\epsilon }_{i}\) are column matrices ...
openaire +1 more source
Psychological methods, 2017
In this article we address a number of important issues that arise in the analysis of nonindependent data. Such data are common in studies in which predictors vary within “units” (e.g., within-subjects, within-classrooms).
M. Brauer, John J. Curtin
semanticscholar +1 more source
In this article we address a number of important issues that arise in the analysis of nonindependent data. Such data are common in studies in which predictors vary within “units” (e.g., within-subjects, within-classrooms).
M. Brauer, John J. Curtin
semanticscholar +1 more source
Linear Mixed Models for Longitudinal Data
Technometrics, 2001A. Avilés
semanticscholar +2 more sources
Generalized linear mixed models: a practical guide for ecology and evolution.
Trends in Ecology & Evolution, 2009Benjamin M. Bolker +6 more
semanticscholar +1 more source
Linear and generalized linear mixed models
2015AbstractGeneralized linear mixed models (GLMMs) are a powerful class of statistical models that combine the characteristics of generalized linear models and mixed models (models with both fixed and random predictor variables). This chapter: reviews the conceptual and theoretical background of GLMMs, focusing on the definition and meaning of random ...
openaire +1 more source
Generalized Linear Mixed Models
2017For analyzing repeated measures data, the necessity of considering the relationships between outcome variables as well as between outcome variables and explanatory variable are of concern. We have discussed about such models in previous chapters. All the models proposed in various chapters are fixed effect models. However, in some cases, the dependence
M. Ataharul Islam, Rafiqul I. Chowdhury
openaire +1 more source
Approximate inference in generalized linear mixed models
, 1993N. Breslow, D. Clayton
semanticscholar +1 more source