An Empirical Study of Statistical Properties of Variance Partition Coefficients for Multi-Level Logistic Regression Models
An Empirical Study of Statistical Properties of Variance Partition Coefficients for Multi-Level Logistic Regression Models
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DOI：
10.1080/03610910802361366
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发表时间：
2008-01-01
影响因子：
0.9
通讯作者：
Bates, Douglas M.
中科院分区：
文献类型：
--
作者：
Li, Jialiang;Gray, Brian R.;Bates, Douglas M.
对于二项式和其他离散结果，按设计水平划分响应方差具有挑战性。Goldstein（2003）提出了两水平Logistic回归模型下方差分配系数（VPC）的四种定义。在这项研究中，我们显式地推导了多水平逻辑回归模型的公式，并随后研究了计算的VPC的分布特性。使用模拟和植被数据集，我们证明了不同的VPC定义之间的关联，用于估计VPC的方法的重要性（通过比较使用拉普拉斯和惩罚拟似然方法获得的VPC），和VPC之间的二元依赖性计算在不同的水平。这样的实证研究提供了直接的支持，更广泛的应用VPC在科学数据分析。
Partitioning the variance of a response by design levels is challenging for binomial and other discrete outcomes. Goldstein (2003) proposed four definitions for variance partitioning coefficients (VPC) under a two-level logistic regression model. In this study, we explicitly derived formulae for multi-level logistic regression model and subsequently studied the distributional properties of the calculated VPCs. Using simulations and a vegetation dataset, we demonstrated associations between different VPC definitions, the importance of methods for estimating VPCs (by comparing VPC obtained using Laplace and penalized quasilikehood methods), and bivariate dependence between VPCs calculated at different levels. Such an empirical study lends an immediate support to wider applications of VPC in scientific data analysis.