@Article{Marcon_etal2013,
author="Marcon, E.
and Traissac, S.
and Lang, G.",
title="A Statistical Test for Ripley{\textquoteright}s Function Rejection of Poisson Null Hypothesis",
journal="ISRN Ecology",
year="2013",
volume="2013",
number="Article ID 753475",
pages="9",
abstract="Ripley{\textquoteright}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.",
optnote="exported from refbase (http://php.ecofog.gf/refbase/show.php?record=479), last updated on Mon, 09 Sep 2013 11:52:12 -0300",
doi="10.1155/2013/753475",
opturl="http://www.hindawi.com/isrn/ecology/2013/753475/cta/",
file=":http://php.ecofog.gf/refbase/files/marcon/2013/479\textit{Marcon}etal2013.pdf:PDF"
}