SpatioTemporal (version 1.1.9.1)

computeLTA: Computes the Long Term Average for Each Sites.

Description

Computes the long term average of observations and cross-validated predictions for each of the sites in object. The long term averages are computed using only timepoints that have observations, this applies to both the observed and predicted. Also the function allows for a transformation: if requested the transformation is applied before the averaging.

Usage

computeLTA(object, transform = function(x) {     return(x) })

Arguments

object

A predCVSTmodel object, the result of predictCV.STmodel.

transform

Transform observations (without bias correction) and predictions before computing averages; e.g. transform=exp gives the long term averages as mean( exp(obs) ) and mean( exp(pred) ).

Value

Returns a (number of locations) - by - 4 matrix with the observed and predicted value (using the three different model parts) for each location.

See Also

Other predCVSTmodel functions: estimateCV.STmodel

Other cross-validation functions: createCV, dropObservations, estimateCV.STmodel, predictNaive

Examples

# NOT RUN {
##load data
data(pred.cv.mesa)

##compute long term averages of predictions and observations
pred.lta <- computeLTA(pred.cv.mesa)

##we can now compare observed and predicted averages at each site
plot(pred.lta[,"obs"], pred.lta[,"EX.mu"], pch=1,
     xlim=range(pred.lta), ylim=range(pred.lta),
     xlab="obs", ylab="predictions")
##for the different model components
points(pred.lta[,"obs"], pred.lta[,"EX.mu.beta"], pch=3, col=2)
points(pred.lta[,"obs"], pred.lta[,"EX"], pch=4, col=3)
abline(0,1)

##we could also try computaitons on the original scale
pred.lta <- computeLTA(pred.cv.mesa, exp)

##compare observed and predicted averages
plot(pred.lta[,"obs"], pred.lta[,"EX.mu"], pch=1,
     xlim=range(pred.lta), ylim=range(pred.lta),
     xlab="obs", ylab="predictions")
points(pred.lta[,"obs"], pred.lta[,"EX.mu.beta"], pch=3, col=2)
points(pred.lta[,"obs"], pred.lta[,"EX"], pch=4, col=3)
abline(0,1)
# }