Nicholas School of the Environment, Duke University, Durham, North Carolina 27708, USA.
Ecology. 2010 Feb;91(2):355-61. doi: 10.1890/09-1043.1.
This paper illustrates the advantages of a multilevel/hierarchical approach for predictive modeling, including flexibility of model formulation, explicitly accounting for hierarchical structure in the data, and the ability to predict the outcome of new cases. As a generalization of the classical approach, the multilevel modeling approach explicitly models the hierarchical structure in the data by considering both the within- and between-group variances leading to a partial pooling of data across all levels in the hierarchy. The modeling framework provides means for incorporating variables at different spatiotemporal scales. The examples used in this paper illustrate the iterative process of model fitting and evaluation, a process that can lead to improved understanding of the system being studied.
本文说明了多层次/分层方法在预测建模中的优势,包括模型公式的灵活性、明确考虑数据中的层次结构,以及预测新病例结果的能力。作为经典方法的推广,多层次建模方法通过考虑组内和组间方差来明确地对数据中的层次结构进行建模,从而导致在层次结构的所有级别上对数据进行部分汇总。该建模框架提供了在不同时空尺度上纳入变量的方法。本文中使用的示例说明了模型拟合和评估的迭代过程,该过程可以帮助更好地理解所研究的系统。