idar (version 1.5)

mitable: Tabulate Marks in Neighbourhood of Every Point in a Point Pattern

Description

For a sequence of radii defining different neighbourhood sizes, visit each point in a focal point pattern, find the neighbouring points in a target point pattern, and compile a frequency table of the marks of these neighbour points for each radii.

Usage

mitable(ppp1,ppp2,r)

Value

A list, with length = length(r). Within each element of the list, a matrix with dimensions np X nsp, where np is the number of points of the focal point pattern and nsp is the number of unique species (i.e., unique marks) in the target point pattern. Cell values in the matrix represent number of neighbours of the j species for individual i at the considered neibourhood size.

Arguments

ppp1

"Focal" point pattern (an ppp object of spatstat) or a data.frame with columns named "x" and "y"

ppp2

"Target" multivariate point pattern (an ppp object of spatstat) or a data.frame with columns named "x", "y", and "marks". The column "marks" wears the (species) name of each point

r

Vector with the sequence of radii (r>0) that define different neiborhood sizes.

Author

Marcelino de la Cruz

Details

Given both a "focal" and a "target" point patterns, mitable visits each point in the focal point pattern, finds the neighbouring points in the target point pattern, and compile a frequency table of the marks of these neighbour points, where the neibourhood is defined by circles orf radius r around the focal points. From an ecological point of view, it provides a relevé of the local community around each focal tree. It repeats the process for each provided r.

See Also

marktable for a similar function.

Examples

# Compute number of trees of different species  within circles of several  radii r 
# around each individual maple tree in lansing woods

data(lansing)

maple<- unmark(lansing[lansing$marks=="maple"])
maple
r= c(0.05, 0.08, 0.1)

counts <- mitable(ppp1=maple, ppp2=lansing, r=r) 
dim(counts[[1]])
head(counts[[1]])