SDALGCPUniformPoint: SDALGCPUniformPoint function

In olatunjijohnson/SDALGCP: Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data

Description

This function generate a random point pattern using a uniform sampling or completely spatial random sampling. An internal function for SDALGCP package.

Usage

SDALGCPUniformPoint(
  poly,
  delta,
  weighted = FALSE,
  pop_shp = NULL,
  lambdamax = NULL,
  pop = NULL,
  n = NULL,
  rho = NULL,
  giveup = NULL,
  bound = NULL
)

Arguments

poly	polygon in which to generate the points
delta	distance between points
weighted	To specify if you want to use the population density, default to FALSE, i.e population density is not used.
pop_shp	Optional, The raster of population density map for population weighted approach
lambdamax	the maximum value of the population density in the polygon
pop	the population density.
n	optional; the number of points to create in the polygon, if not supplied, it is computed as n = rho*|A|*4/(π*delta^2)
rho	Optional, The packing density, default set to 0.55
giveup	Number of rejected proposals after which the algorithm should terminate.
bound	Spatial object; the boundary of the polygon

Details

This algorithm generates points inside the polygon using a uniform sampling or completely spatial random sampling.

Value

It returns a list of the coordinates of the points created in each polygon.

Author(s)

Olatunji O. Johnson [e-mail]

Emanuele Giorgi [e-mail]

Peter J. Diggle [e-mail]

Examples

data(PBCshp)
require(sp)  #load sp package
poly <- PBCshp@polygons[[1]]@Polygons[[1]]@coords
#create a spatialpolygons object
bound <- SpatialPolygons(list(Polygons(list(Polygon(poly)), "x")))
point <- SDALGCPUniformPoint(poly=poly, delta=100, n=1, bound = bound)

olatunjijohnson/SDALGCP documentation built on March 20, 2021, 4:24 a.m.