Department of Mathematics
A very common way of analyzing different and complicated plant behaviors is to use spatial point pattern analysis, which allows us to assess whether there is any structure present. To test the complete spatial randomness hypothesis, Diggle (1979Diggle , P. J. ( 1979 ). On parameter estimation and goodness-of-fit testing for spatial point patterns . Biometrics 35 : 87 – 101 .[CrossRef], [Web of Science ®], [Google Scholar]) proposed a Monte Carlo test whose test statistic is the discrepancy between the estimated and the theoretical form of some summary function, such as the Ripley K-function. In this article, we improve this test by adding various weight functions and get more powerful tests if decreasing and increasing weight functions are used for processes with short and long, respectively, range of interaction.
Complete spatial randomness, Edge-correction, K-function, Monte Carlo simulation
Source Publication Title
Communications in Statistics - Simulation and Computation
Taylor & Francis
This is an Accepted Manuscript of an article published by Taylor & Francis in Communications in Statistics - Simulation and Computation in January 2009, available online: http://dx.doi.org/10.1080/03610910802478343.
This research was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Nos. HKBU2048/02P and HKBU200503) and an FRG grant of the Hong Kong Baptist University.
Link to Publisher's Edition
Ho, L., & Chiu, S. (2008). Using weight functions in spatial point pattern analysis with application to plant ecology data. Communications in Statistics - Simulation and Computation, 38 (2), 269-287. https://doi.org/10.1080/03610910802478343