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ecespa (version 1.1-9)

Functions for Spatial Point Pattern Analysis

Description

Some wrappers, functions and data sets for for spatial point pattern analysis (mainly based on 'spatstat'), used in the book "Introduccion al Analisis Espacial de Datos en Ecologia y Ciencias Ambientales: Metodos y Aplicaciones" and in the papers by De la Cruz et al. (2008) and Olano et al. (2009) .

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Version

Install

install.packages('ecespa')

Monthly Downloads

621

Version

1.1-9

License

GPL (>= 2)

Maintainer

Marcelino la Cruz

Last Published

November 24th, 2017

Functions in ecespa (1.1-9)

Tests against 'independent labelling'

Functions for spatial point pattern analysis in ecology

Loosmore and Ford Goodness of Fit Test

Dixon (2002) Nearest-neighbor contingency table analysis

Differences between univariate and bivariate K-functions

Spatial point pattern of Helianthemum squamatum adult plants and seedlings

Simulation envelopes from the fitted values of a logistic model

Test against non-Poisson (in-)homogeneous models

ecespa-internal

Internal ecespa functions.

Mark-weighted K-function

Easily convert xy data to ppp format

Lotwick's and Silverman's combined estimator of the marked K-function

Spatial point pattern of a plant community

Artificial point data.

Fit the (In)homogeneous Poisson Cluster Point Process by Minimum Contrast

Mark-sum measure

Neighbourhood density function

P-value for a discrete distribution on small sample data

Cohorts of Helianthemum squamatum seedlings

Fit the Poisson Cluster Point Process by Minimum Contrast

Alive and dead oak trees

Simulate Poisson Cluster Process

Simulate Inhomogeneous Poisson Cluster Process

Syrjala's test for the difference between the spatial distributions of two populations

Syrjala test data

Tree Species in a Swamp Forest