How to make models add up - a primer on GLMMs
How to make models add up - a primer on GLMMs
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DOI：
10.5735/086.046.0205
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发表时间：
2009-04-30
影响因子：
0.7
通讯作者：
O'Hara, Robert B.
中科院分区：
文献类型：
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作者：
O'Hara, Robert B.
生态数据分析中的许多问题都有这样的格式，即存在可由几个协变量预测的观测响应。虽然响应可以采取几种形式（例如测量，计数，存在/不存在的观察），并且协变量也可以变化（例如，测量本身，或根据应用的处理，采样的时间或地点进行分组等），但大多数这些问题都可以在单一框架中处理，即广义线性混合模型（GLMM）。该框架包括回归、方差分析、广义线性模型以及具有随机效应和固定效应的等效模型。在这里，描述了GLMM的不同部分，从回归和方差分析的基础上，展示了如何将额外的成分——更广泛的分布范围和随机效应——添加到同一个框架中，以及如何估计和解释拟合模型的参数。能够使用glmm处理数据有助于生态学家分析他们的大部分数据。
Many problems in the analysis of ecological data have the format where there is an observed response that may be predicted by several covariates. Although the response can take several forms (e.g. measurements, counts, observations of presence/absence), and the covariates can also vary (e.g. be measurements themselves, or be grouped according to the treatment applied, the time or location of of sampling, etc.), most of these problems can be handled in a single framework, the Generalized Linear Mixed Model (GLMM). The framework encompasses regression, ANOVA, generalized linear models, and equivalent models with random as well as fixed effects. Here, the different parts of the GLMM are described, building from regression and ANOVA to show how the extra components - the wider range of distributions, and random effects - can be added into the same framework, and how the parameters of the fitted model can be estimated and interpreted. Being able to handle data with GLMMs helps ecologists to analyse the majority of their data.