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<title lang=en>A statistical test for ripley's K function rejection of poisson null hypothesis</title>
<creator>Marcon, Eric</creator>
<creator>Traissac, Stephane</creator>
<creator>Lang, Gabriel</creator>
<contributor>Ecologie des forêts de Guyane (ECOFOG) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - Institut National de la Recherche Agronomique (INRA) - Université des Antilles et de la Guyane (UAG) - AgroParisTech - Université de Guyane (UG) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>INRA - Mathématiques et Informatique Appliquées (Unité MIAJ) ; Institut National de la Recherche Agronomique (INRA)</contributor>
<contributor>Mathématiques et Informatique Appliquées (MIA-Paris) ; Institut National de la Recherche Agronomique (INRA) - AgroParisTech</contributor>
<description>International audience</description>
<source>International Scholarly Research Network, ISRN Ecology</source>
<identifier>hal-01502637</identifier>
<identifier>https://hal-agroparistech.archives-ouvertes.fr/hal-01502637</identifier>
<identifier>https://hal-agroparistech.archives-ouvertes.fr/hal-01502637/document</identifier>
<identifier>https://hal-agroparistech.archives-ouvertes.fr/hal-01502637/file/2013_Marcon_Hindawi%20Publishing%20Corporation_%7B7A602F34-6983-47DE-924B-7CDC3B8D14F7%7D.pdf</identifier>
<source>https://hal-agroparistech.archives-ouvertes.fr/hal-01502637</source>
<source>International Scholarly Research Network, ISRN Ecology, 2013, 2013 (Article ID753475), 9 p. 〈10.1155/2013/753475〉</source>
<identifier>DOI : 10.1155/2013/753475</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1155/2013/753475</relation>
<language>en</language>
<subject lang=en>statistical test</subject>
<subject lang=en>ripley’s K function rejection</subject>
<subject lang=en>poisson</subject>
<subject>[STAT] Statistics [stat]</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>Ripley’s K function is the classical tool to characterize the spatial structure of point patterns. It is widely used in vegetation studies. Testing its values against a null hypothesis usually relies on Monte-Carlo simulations since little is known about its distribution.We introduce a statistical test against complete spatial randomness (CSR). The test returns the p-value to reject the null hypothesis of independence between point locations. It is more rigorous and faster than classical Monte-Carlo simulations. We show how to apply it to a tropical forest plot. The necessary R code is provided.</description>
<date>2013-01-15</date>
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