excProb {geostatsp} | 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') 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)) ## Not run: 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) ## End(Not run)