Demattei Christophe, Molinari Nicolas, Daurès Jean-Pierre
Laboratoire de biostatistique, d'Epidémiologie et de Santé Publique, UFR Médecine Site Nord IURC, 641 Avenue du Doyen Gaston Giraud, 34093 Montpellier Cedex 5, France.
Comput Methods Programs Biomed. 2006 Oct;84(1):42-9. doi: 10.1016/j.cmpb.2006.07.008. Epub 2006 Sep 11.
This paper describes an R package, named SPATCLUS that implements a method recently proposed for spatial cluster detection of case event data. This method is based on a data transformation. This transformation is achieved by the definition of a trajectory, which allows to attribute to each point a selection order and the distance to its nearest neighbour. The nearest point is searched among the points which have not yet been selected in the trajectory. Due to the trajectory effects, the distance is weighted by the expected distance under the uniform distribution hypothesis. Potential clusters are located by using multiple structural change models and a dynamic programming algorithm. The double maximum test allows to select the best model. The significativity of potential clusters is determined by Monte Carlo simulations. This method makes it possible the detection of multiple clusters of any shape.
本文描述了一个名为SPATCLUS的R软件包，它实现了一种最近提出的用于病例事件数据空间聚类检测的方法。该方法基于一种数据变换。这种变换是通过定义一条轨迹来实现的，该轨迹允许为每个点赋予一个选择顺序及其到最近邻点的距离。最近点是在轨迹中尚未被选中的点中搜索的。由于轨迹效应，距离在均匀分布假设下的预期距离进行加权。通过使用多个结构变化模型和动态规划算法来定位潜在聚类。双重最大检验允许选择最佳模型。潜在聚类的显著性通过蒙特卡罗模拟来确定。这种方法使得检测任何形状的多个聚类成为可能。
