GET (version 0.2)

GET.spatialF: Testing global and local dependence of point patterns on covariates

Description

Compute the spatial F- and S-statistics and perform the one-stage global envelope tests proposed by Myllym<U+00E4>ki et al. (2020).

Usage

GET.spatialF(
  X,
  formula.full,
  formula.reduced,
  fitfun,
  covariates,
  nsim,
  bw = spatstat::bw.scott(X),
  bw.S = bw,
  dimyx = NULL,
  ...
)

Arguments

X

A ppp object representing the observed point pattern.

formula.full

A formula for the trend of the full model.

formula.reduced

A formula for the trend of the reduced model that is a submodel of the full model.

fitfun

A function of a point pattern, model formula and covariates, giving a fitted model object that can be used with simulate.

covariates

A list of covariates.

nsim

The number of simulations.

bw

The bandwidth for smoothed residuals.

bw.S

The radius for the local S(u)-statistic.

dimyx

Pixel array dimensions for smoothed residuals. See as.mask.

...

Additional arguments to be passed to global_envelope_test.

Value

list with three components

• F = the global envelope test based on the F(u) statistic

• S = the global envelope test based on the S(u) statistic

• coef = the coefficients of the full model given by fitfun

References

Myllym<U+00E4>ki, M., Kuronen, M. and Mrkvi<U+010D>ka, T. (2020). Testing global and local dependence of point patterns on covariates in parametric models. Spatial Statistics. doi: 10.1016/j.spasta.2020.100436

Examples

# NOT RUN {
if(require("spatstat", quietly=TRUE)) {
  # Example of tropical rain forest trees
  data(bei)

  fullmodel <- ~ grad
  reducedmodel <- ~ 1
  fitppm <- function(X, model, covariates) {
    ppm(X, model, covariates=covariates)
  }
  
# }
# NOT RUN {
  
# }
# NOT RUN {
  nsim <- 19 # Increase nsim for serious analysis!
  res <- GET.spatialF(bei, fullmodel, reducedmodel, fitppm, bei.extra, nsim)
  
# }
# NOT RUN {
  plot(res$F)
  plot(res$S)

  
# }
# NOT RUN {
  # Example of forest fires
  data("clmfires")
  # Choose the locations of the lightnings in years 2004-2007:
  pp.lightning <- unmark(subset(clmfires, cause == "lightning" &
                   date >= "2004-01-01" & date < "2008-01-01"))

  covariates <- clmfires.extra$clmcov100
  covariates$forest <- covariates$landuse == "conifer" | covariates$landuse == "denseforest" |
                        covariates$landuse == "mixedforest"

  fullmodel <- ~ elevation + landuse
  reducedmodel <- ~ landuse
  nsim <- 19 # Increase nsim for serious analysis!
  res <- GET.spatialF(pp.lightning, fullmodel, reducedmodel, fitppm, covariates, nsim)
  plot(res$F)
  plot(res$S)

  # Examples of the fitfun functions for clustered and regular processes
  # fitfun for the log Gaussian Cox Process with exponential covariance function
  fitLGCPexp <- function(X, model, covariates) {
    kppm(X, model, clusters="LGCP", model="exponential", covariates=covariates)
  }
  # fitfun for the hardcore process with hardcore radius 0.01
  fitHardcore <- function(X, model, covariates) {
    ppm(X, model, interaction=Hardcore(0.01), covariates = covariates)
  }
  
# }
# NOT RUN {
}
# }