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 (Citation1979) 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.
|Number of pages||19|
|Journal||Communications in Statistics - Simulation and Computation|
|Early online date||22 Dec 2008|
|Publication status||Published - Mar 2009|
- Complete spatial randomness
- Monte Carlo simulation