Document Detail


Statistical inference using the g or K point pattern spatial statistics.
MedLine Citation:
PMID:  16937629     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Spatial point pattern analysis provides a statistical method to compare an observed spatial pattern against a hypothesized spatial process model. The G statistic, which considers the distribution of nearest neighbor distances, and the K statistic, which evaluates the distribution of all neighbor distances, are commonly used in such analyses. One method of employing these statistics involves building a simulation envelope from the result of many simulated patterns of the hypothesized model. Specifically, a simulation envelope is created by calculating, at every distance, the minimum and maximum results computed across the simulated patterns. A statistical test is performed by evaluating where the results from an observed pattern fall with respect to the simulation envelope. However, this method, which differs from P. Diggle's suggested approach, is invalid for inference because it violates the assumptions of Monte Carlo methods and results in incorrect type I error rate performance. Similarly, using the simulation envelope to estimate the range of distances over which an observed pattern deviates from the hypothesized model is also suspect. The technical details of why the simulation envelope provides incorrect type I error rate performance are described. A valid test is then proposed, and details about how the number of simulated patterns impacts the statistical significance are explained. Finally, an example of using the proposed test within an exploratory data analysis framework is provided.
Authors:
N Bert Loosmore; E David Ford
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Ecology     Volume:  87     ISSN:  0012-9658     ISO Abbreviation:  Ecology     Publication Date:  2006 Aug 
Date Detail:
Created Date:  2006-08-29     Completed Date:  2006-12-22     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0043541     Medline TA:  Ecology     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1925-31     Citation Subset:  IM    
Affiliation:
Quantitative Ecology and Resource Management, Box 352182, University of Washington, Seattle, Washington 98195-2182, USA. nhl@u.washington.edu
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MeSH Terms
Descriptor/Qualifier:
Data Interpretation, Statistical*
Models, Statistical
Monte Carlo Method
Trees

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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