EOT based spatial prediction

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Description

Make spatial predictions using the fitted model returned by eot. A (user-defined) set of n modes will be used to model the outcome using the identified link functions of the respective modes which are added together to produce the final prediction.

Usage

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## S4 method for signature 'EotStack'
predict(object, newdata, n = 1, ...)

## S4 method for signature 'EotMode'
predict(object, newdata, n = 1, ...)

Arguments

object

an Eot* object

newdata

the data to be used as predictor

n

the number of modes to be used for the prediction. See nXplain for calculating the number of modes based on their explnatory power.

...

further arguments to be passed to calc

Value

a RasterStack of nlayers(newdata)

Methods (by class)

  • EotMode: EotMode

Examples

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### not very useful, but highlights the workflow
data(pacificSST)
data(australiaGPCP)

## train data using eot()
train <- eot(x = pacificSST[[1:10]], 
             y = australiaGPCP[[1:10]], 
             n = 1)

## predict using identified model
pred <- predict(train, 
                newdata = pacificSST[[11:20]], 
                n = 1)

## compare results
opar <- par(mfrow = c(1,2))
plot(australiaGPCP[[13]], main = "original", zlim = c(0, 10))
plot(pred[[3]], main = "predicted", zlim = c(0, 10))
par(opar)