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gllvm 2.0:快速适应先进的排序方法和联合物种分布模型

Pekka Korhonen¹, Francis K C Hui², Jenni Niku³

¹Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland.

|December 17, 2025

概括

联合物种分布模型 (JSDMs) 和通用线性潜变量模型 (GLLVMs) 分析社区生态数据. R包gllvm为GLLVM提供了增强的功能,改善了生态数据分析.

科学领域:

生态生态学生态生态学
计算生物学计算生物学
统计统计统计统计

背景情况:

联合物种分布模型 (JSDMs) 和基于模型的排序是社区生态学的关键工具.
一般化的线性潜变量模型 (GLLVMs) 为各种数据类型的多变量分析提供了灵活的框架.
GLLVMs使用潜在变量来减少尺寸,并考虑物种相关性.

研究的目的:

引入更新的 R 包 gllvm,这是 GLLVM 的一个全面框架.
要突出该包的增强功能和估计方法用于生态数据分析.
为先进的社区生态建模提供可访问的工具.

主要方法:

该gllvm套件使用基于概率的快速近似估计来实现gllvm.
使用扩展变量近似,支持各种响应类型和链接函数.
该软件包促进了基于模型的排序,层次设计和族系学分析.

主要成果:

全球生物科学计划 (gllvm) 已经得到了重大改革,为JSDMs提供了一个强大的框架.
新功能包括受约束/并发排序,对嵌套设计的处理,以及家族遗传随机效应.
包括扩展的响应类型处理和改进的推断,选择和预测能力.

结论:

更新的gllvm软件包是分析复杂的生态社区数据的强大而多功能工具.
它的增强功能和估计方法促进了GLLVM在生态学的先进应用.
该套件简化了各种生态建模场景的安装和预测.

关键词:

联合建模联合建模最大的可能性.多变量丰度数据多变量丰度数据嵌套的设计嵌套的设计圣职指令圣职指令遗传学混合模型遗传学混合模型在这个过程中,R是R.软件软件软件软件软件

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