simLgcp {geostatsp} | R Documentation |
Give covariates and model parameters, simulates a log-Gaussian Cox process
simLgcp(param, covariates=NULL, betas=NULL, offset=NULL, rasterTemplate=covariates[[1]], n=1, ...) simPoissonPP(intensity)
param |
A vector of named model parameters with, at a minimum names
|
covariates |
Either a raster stack or list of rasters and SpatialPolygonsDataFrames (with the latter having only a single data column). |
betas |
Coefficients for the covariates |
offset |
Vector of character strings corresponding to elements of |
rasterTemplate |
Raster on which the latent surface is simulated, defaults to the first covariate. |
n |
number of realisations to simulate |
... |
additional arguments, see |
intensity |
Raster of the intensity of a Poisson point process. |
A list with a events
element containing the event locations and a raster
element
containing a raster stack of the covariates, spatial random effect, and intensity.
mymodel = c(mean=-0.5, variance=1, range=2, shape=2) myraster = raster(nrows=15,ncols=20,xmn=0,xmx=10,ymn=0,ymx=7.5) # some covariates, deliberately with a different resolution than myraster covA = covB = myoffset = raster(extent(myraster), 10, 10) values(covA) = as.vector(matrix(1:10, 10, 10)) values(covB) = as.vector(matrix(1:10, 10, 10, byrow=TRUE)) values(myoffset) = round(seq(-1, 1, len=ncell(myoffset))) myCovariate = list(a=covA, b=covB, offsetFooBar = myoffset) myLgcp=simLgcp(param=mymodel, covariates=myCovariate, betas=c(a=-0.1, b=0.25), offset='offsetFooBar', rasterTemplate=myraster) plot(myLgcp$raster[["intensity"]], main="lgcp") points(myLgcp$events) myIntensity = exp(-1+0.2*myCovariate[["a"]]) myPoissonPP = simPoissonPP(myIntensity)[[1]] plot(myIntensity, main="Poisson pp") points(myPoissonPP)