MCMC for Generalized Linear Mixed Models with glmmBUGS

被引：0

作者：

Brown, Patrick ^{[1
,2
]}

Zhou, Lutong ^{[2
]}

机构：

[1] Univ Toronto, Dalla Lana Sch Publ Hlth, Toronto, ON M5S 1A1, Canada

[2] Canc Care Ontario, Toronto, ON, Canada

来源：

R JOURNAL | 2010年 / 2卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The glmmBUGS package is a bridging tool between Generalized Linear Mixed Models (GLMMs) in R and the BUGS language. It provides a simple way of performing Bayesian inference using Markov Chain Monte Carlo (MCMC) methods, taking a model formula and data frame in R and writing a BUGS model file, data file, and initial values files. Functions are provided to reformat and summarize the BUGS results. A key aim of the package is to provide files and objects that can be modified prior to calling BUGS, giving users a platform for customizing and extending the models to accommodate a wide variety of analyses.

引用

页码：13 / 17

页数：5

共 50 条

[41] GENERALIZED FIDUCIAL INFERENCE FOR NORMAL LINEAR MIXED MODELS
Cisewski, Jessi
Hannig, Jan
ANNALS OF STATISTICS, 2012, 40 (04): : 2102 - 2127
[42] Misspecification of the covariance structure in generalized linear mixed models
Chavance, M.
Escolano, S.
STATISTICAL METHODS IN MEDICAL RESEARCH, 2016, 25 (02) : 630 - 643
[43] Parsimonious Classification Via Generalized Linear Mixed Models
G. Kauermann
J. T. Ormerod
M. P. Wand
Journal of Classification, 2010, 27 : 89 - 110
[44] Conditional likelihood inference in generalized linear mixed models
Sartori, N
Severini, TA
STATISTICA SINICA, 2004, 14 (02) : 349 - 360
[45] Laplace importance sampling for generalized linear mixed models
Kuk, AYC
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 1999, 63 (02) : 143 - 158
[46] Generalized linear mixed models: a review and some extensions
Dean, C. B.
Nielsen, Jason D.
LIFETIME DATA ANALYSIS, 2007, 13 (04) : 497 - 512
[47] Estimation of group means in generalized linear mixed models
Duan, Jiexin
Levine, Michael
Luo, Junxiang
Qu, Yongming
PHARMACEUTICAL STATISTICS, 2020, 19 (05) : 646 - 661
[48] General design Bayesian generalized linear mixed models
Zhao, Y.
Staudenmayer, J.
Coull, B. A.
Wand, M. P.
STATISTICAL SCIENCE, 2006, 21 (01) : 35 - 51
[49] Efficient Shrinkage for Generalized Linear Mixed Models Under Linear Restrictions
Thomson T.
Hossain S.
Sankhya A, 2018, 80 (2): : 385 - 410
[50] Testing polynomial covariate effects in linear and generalized linear mixed models
Huang, Mingyan
Zhang, Daowen
STATISTICS SURVEYS, 2008, 2 : 154 - 169

← 1 2 3 4 5 →