pointPattern: Neighbours Functions and Spatial Point Patterns

In adalardo/Rppsp: R functions to manage permanent plot data (short line)

Neighbours Functions and Spatial Point Patterns

Description

Functions to analysis population spatial arrangement and respective graphics

Usage

xyTest(x, y)

limTest(x, y, xlim, ylim)

minContrast(xyStat, par)

estimaThomas(x, y, xlim, ylim, parIni = NULL)

Arguments

x, y	Numeric vectors of cartesian coordinates of neighbor points.
xlim, ylim	Numeric vectors with two values defining the 'x' and 'y' axis limit in the cartesian plane.
xyStat	Point pattern statistics dataframe.
par	Cluster Thomas process parameters: $\kappa$, $\sigma$.
parIni	Initial parameters used by [optim()] to estimate Thomas Process parameters.
xc, yc	Numeric scalar coordinates of focus point.
r	Numeric neighborhood radius.

Details

A set of functions to analysis point patterns process based on K-Ripley statistics for univariate and bivariate type of points. Originally these functions were coded for a study biological populations of sessils organisms, but can be apply for any cartesian mapping objects. Thoses functions are first coded for EcoVirtual Project http://ecovirtual.ib.usp.br.

'neighbors' identifies neighbors around all points in a cartesian coordinate space. 'countNeighbors' counts the number of neighbors for each point for a given r distance.

'edgeData' torus border data adjustment.

'meanNeighbors' calculate the mean number of neighbors for a sequence of r distances.

'meanNeighborsBi' calculate the mean number of neighbors for a sequence of r distances for a bivariate points. First code represents the target points and second level code the neighbors points.

'ppStats' calculate K-Ripley, L-Ripley and O-Ring statistics for univariate points in a cartesian coordinate space for a sequence of r distances.

'ppStatsBi' calculate K-Ripley, L-Ripley and O-Ring statistics for bivariate points in a cartesian coordinate space for a sequence of r distances.

'minContrast' calculate mininum contrasts for estimate parameters for cluster Thomas Process.

'estimaThomas' estimates the Thomas process function parameters

'envelopeSim' calculates Monte Carlo confidence envelops for complete spatial random or cluster Thomas process.

'envelopeSimBi' calculates Monte Carlo confidence envelops for complete spatial random bivariate point process.

Value

The functions return graphics with the simulation results, and a matrix with the population size for deterministic and stochastic models.

Author(s)

Alexandre Adalardo de Oliveira [e-mail]

References

Baddeley, A.; Rubak, E; Turner, R. 2016. Spatial Point Patterns: Methodology and Applications with R. CRC Press. Wiegand, T. & Moloney, K.A. 2014. Handbook of Spatial Point-Pattern Analysis in Ecology. CRC Press.

See Also

metaComp, http://ecovirtual.ib.usp.br

adalardo/Rppsp documentation built on June 10, 2025, 1:11 p.m.