| excProb | R Documentation | 
Calculate exceedance probabilities pr(X > threshold) from a fitted geostatistical model.
excProb(x, threshold=0, random=FALSE, template=NULL, templateIdCol=NULL,
nuggetInPrediction=TRUE)
| x | Output from either the  | 
| threshold | the value which the exceedance probability is calculated with respect to. | 
| random | Calculate exceedances for the random effects, rather than the predicted observations (including fixed effects). | 
| template | A  | 
| templateIdCol | The data column in  | 
| nuggetInPrediction | If  | 
When x is the output from lgm, pr(Y>threshold) is calculated using
the Gaussian distribution using the Kriging mean and conditional variance.  When
x is from the glgm function, 
the marginal posteriors are numerically integrated to obtain pr(X > threshold).
Either a vector of exceedance probabilities or an object of the same class as template.
	data('swissRain')
	swissRain = unwrap(swissRain)
	swissAltitude = unwrap(swissAltitude)
	swissBorder = unwrap(swissBorder)
	swissFit =  lgm("rain", swissRain, grid=30, 
		boxcox=0.5,fixBoxcox=TRUE,	covariates=swissAltitude)
	swissExc = excProb(swissFit, 20)
	mycol = c("green","yellow","orange","red")
	mybreaks = c(0, 0.2, 0.8, 0.9, 1)
	plot(swissBorder)
	plot(swissExc, breaks=mybreaks, col=mycol,add=TRUE,legend=FALSE)
	plot(swissBorder, add=TRUE)
	legend("topleft",legend=mybreaks, col=c(NA,mycol))
if(requireNamespace("INLA", quietly=TRUE) ) {
  INLA::inla.setOption(num.threads=2)
  # not all versions of INLA support blas.num.threads
  try(INLA::inla.setOption(blas.num.threads=2), silent=TRUE)
	swissRain$sqrtrain = sqrt(swissRain$rain)
	swissFit2 =  glgm(formula="sqrtrain",data=swissRain, grid=40, 
	covariates=swissAltitude,family="gaussian")
	swissExc = excProb(swissFit2, threshold=sqrt(30))
	swissExc = excProb(swissFit2$inla$marginals.random$space, 0,
		template=swissFit2$raster)
	
}
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