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Calibrate the AOA based on the relationship between the DI and the prediction error

Source: R/calibrate_aoa.R
calibrate_aoa.Rd

Performance metrics are calculated for moving windows of DI values of cross-validated training data

Usage

calibrate_aoa(
  AOA,
  model,
  window.size = 5,
  calib = "scam",
  multiCV = FALSE,
  length.out = 10,
  maskAOA = TRUE,
  method = "L2",
  useWeight = TRUE,
  showPlot = TRUE,
  k = 6,
  m = 2
)

Arguments

AOA

the result of aoa

model

the model used to get the AOA

window.size

Numeric. Size of the moving window. See rollapply.

calib

Character. Function to model the DI~performance relationship. Currently lm and scam are supported

multiCV

Logical. Re-run model fitting and validation with different CV strategies. See details.

length.out

Numeric. Only used if multiCV=TRUE. Number of cross-validation folds. See details.

maskAOA

Logical. Should areas outside the AOA set to NA?

method

Character. Method used for distance calculation. Currently euclidean distance (L2) and Mahalanobis distance (MD) are implemented but only L2 is tested. Note that MD takes considerably longer. See ?aoa for further explanation

useWeight

Logical. Only if a model is given. Weight variables according to importance in the model?

showPlot

Logical.

k

Numeric. See mgcv::s

m

Numeric. See mgcv::s

Value

A list of length 2 with the elements "AOA": SpatRaster or stars object which contains the original DI and the AOA (which might be updated if new test data indicate this option), as well as the expected performance based on the relationship. Data used for calibration are stored in the attributes. The second element is a plot showing the relationship.

Details

If multiCV=TRUE the model is re-fitted and validated by length.out new cross-validations where the cross-validation folds are defined by clusters in the predictor space, ranging from three clusters to LOOCV. Hence, a large range of DI values is created during cross-validation. If the AOA threshold based on the calibration data from multiple CV is larger than the original AOA threshold (which is likely if extrapolation situations are created during CV), the AOA is updated accordingly. See Meyer and Pebesma (2021) for the full documentation of the methodology.

References

Meyer, H., Pebesma, E. (2021): Predicting into unknown space? Estimating the area of applicability of spatial prediction models. doi:10.1111/2041-210X.13650

See also

aoa

Author

Hanna Meyer

Examples

if (FALSE) {
library(sf)
library(terra)
library(caret)
library(viridis)
library(latticeExtra)

#' # prepare sample data:
data(cookfarm)
dat <- aggregate(cookfarm[,c("VW","Easting","Northing")],
    by=list(as.character(cookfarm$SOURCEID)),mean)
pts <- st_as_sf(dat,coords=c("Easting","Northing"))
pts$ID <- 1:nrow(pts)
studyArea <- rast(system.file("extdata","predictors_2012-03-25.tif",package="CAST"))[[1:8]]
dat <- extract(studyArea,pts,na.rm=TRUE)
trainDat <- merge(dat,pts,by.x="ID",by.y="ID")

# train a model:
variables <- c("DEM","NDRE.Sd","TWI")
set.seed(100)
model <- train(trainDat[,which(names(trainDat)%in%variables)],
  trainDat$VW,method="rf",importance=TRUE,tuneLength=1,
  trControl=trainControl(method="cv",number=5,savePredictions=TRUE))

#...then calculate the AOA of the trained model for the study area:
AOA <- aoa(studyArea,model)

AOA_new <- calibrate_aoa(AOA,model)
plot(AOA_new$AOA$expected_RMSE)
}