Nothing
posPredictionInterval = function(krige_object, p = 95,
value = median(krige_object$krige_output$var1.pred))
# This function gives the position of the "p"% prediction interval
# relative to "value".
{
if(!inherits(krige_object,"autoKrige")) stop("Invalid input object, object should be of class autoKrige")
# It is assumed that the kriging error is distributed normally around the kriging prediction.
p_in_tail = (100 - p) / 2 # The probability left in each tail (upper and lower)
# An assumption is made that the kriging prediction and variance are located in the
# first and second column of krige_object.
# Prediction # Standard deviation
krige_object$krige_output$lower = krige_object$krige_output[[1]] + qnorm(p_in_tail / 100)*sqrt(krige_object$krige_output[[2]])
krige_object$krige_output$upper = krige_object$krige_output[[1]] + qnorm((100 - p_in_tail) / 100)*sqrt(krige_object$krige_output[[2]])
position = function(upper,lower,value)
# This function returns the status for each gridcell in nl_grid
# "above", "below", "unclear" for the p% prediction interval
{
if(lower > value) return("higher")
if(upper < value) return("lower")
else return("not distinguishable")
}
krige_object$krige_output$position = factor(mapply(FUN = position, krige_object$krige_output$upper, krige_object$krige_output$lower,value))
result = list(pos_prediction = krige_object$krige_output[c("upper","lower","position")], p = p, value = value)
class(result) = c("posPredictionInterval","list")
return(result)
}
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