MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package
MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package
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DOI：
10.18637/jss.v033.i02
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发表时间：
2010-02-01
影响因子：
5.8
通讯作者：
Hadfield, Jarrod D.
中科院分区：
文献类型：
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作者：
Hadfield, Jarrod D.
广义线性混合模型为一系列数据的建模提供了一个灵活的框架，尽管对于非高斯响应变量，可能无法以封闭形式获得。马尔可夫链蒙特卡罗方法通过从一系列可求值的简单条件分布中抽样来解决这个问题。R包M C C g l M M实现了这种算法，用于一系列模型拟合问题。可以同时分析多个响应变量，并且允许这些变量遵循高斯分布、泊松分布、多（bi）标称分布、指数分布、零膨胀分布和截尾分布。随机效应允许一系列变异结构，包括与分类变量或连续变量的相互作用（即随机回归），以及通过共同祖先产生的更复杂的变异结构，无论是通过系谱还是通过系统发育。在响应变量中允许存在缺失值，并且在元分析中，数据可以在某种程度上存在测量误差。所有的模拟都是在C/ c++中使用稀疏线性系统的CSparse库完成的。
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. Markov chain Monte Carlo methods solve this problem by sampling from a series of simpler conditional distributions that can be evaluated. The R package M C M C g l m m implements such an algorithm for a range of model fitting problems. More than one response variable can be analyzed simultaneously, and these variables are allowed to follow Gaussian, Poisson, multi(bi) nominal, exponential, zero-inflated and censored distributions. A range of variance structures are permitted for the random effects, including interactions with categorical or continuous variables (i.e., random regression), and more complicated variance structures that arise through shared ancestry, either through a pedigree or through a phylogeny. Missing values are permitted in the response variable(s) and data can be known up to some level of measurement error as in meta-analysis. All simulation is done in C/C++ using the CSparse library for sparse linear systems.