remote (version 1.2.3)

predict: EOT based spatial prediction

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

# S4 method for EotStack
predict(object, newdata, n = 1, cores = 1L, filename = "", ...)
# S4 method for EotMode
predict(object, newdata, n = 1, cores = 1L, filename = "", ...)

Value

a RasterStack of nlayers(newdata)

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 explanatory power.

cores

integer. Number of cores for parallel processing.

filename

character, output filenames (optional). If specified, this must be of the same length as nlayers(newdata).

...

further arguments passed to raster::calc(), and hence, raster::writeRaster().

See Also

raster::calc(), raster::writeRaster().

Examples

### not very useful, but highlights the workflow
# \donttest{
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)
# }