Description Usage Arguments Value Author(s) See Also Examples
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.
1 | computeLTA(object, transform = function(x) { return(x) })
|
object |
A |
transform |
Transform observations (without bias correction) and
predictions before
computing averages; e.g. |
Returns a (number of locations) - by - 4 matrix with the observed and predicted value (using the three different model parts) for each location.
Johan Lindstrom
Other predCVSTmodel functions: estimateCV.STmodel
Other cross-validation functions: createCV
,
dropObservations
,
estimateCV.STmodel
,
predictNaive
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ##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)
|
Loading required package: Matrix
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