Model and inspect the relationship between the prediction error and measures of dissimilarities and distances

Performance metrics are calculated for moving windows of dissimilarity values based on cross-validated training data

Usage

errorProfiles(
  model,
  trainDI = NULL,
  locations = NULL,
  variable = "DI",
  multiCV = FALSE,
  length.out = 10,
  window.size = 5,
  calib = "scam",
  method = "L2",
  useWeight = TRUE,
  k = 6,
  m = 2
)

Arguments

model

the model used to get the AOA

trainDI

the result of trainDI or aoa object aoa

locations

Optional. sf object for the training data used in model. Only used if variable=="geodist". Note that they must be in the same order as model$trainingData.

variable

Character. Which dissimilarity or distance measure to use for the error metric. Current options are "DI" or "LPD"

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.

window.size

Numeric. Size of the moving window. See rollapply.

calib

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

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?

k

Numeric. See mgcv::s

m

Numeric. See mgcv::s

Value

A scam, linear model or exponential model

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 dissimilarity 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 threshold changes 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, Marvin Ludwig, Fabian Schumacher

Examples

if (FALSE) { # \dontrun{
library(CAST)
library(sf)
library(terra)
library(caret)

data(splotdata)
predictors <- terra::rast(system.file("extdata","predictors_chile.tif", package="CAST"))

model <- caret::train(st_drop_geometry(splotdata)[,6:16], splotdata$Species_richness,
   ntree = 10, trControl = trainControl(method = "cv", savePredictions = TRUE))

AOA <- aoa(predictors, model, LPD = TRUE, maxLPD = 1)

### DI ~ error
errormodel_DI <- errorProfiles(model, AOA, variable = "DI")
plot(errormodel_DI)
summary(errormodel_DI)

expected_error_DI = terra::predict(AOA$DI, errormodel_DI)
plot(expected_error_DI)

### LPD ~ error
errormodel_LPD <- errorProfiles(model, AOA, variable = "LPD")
plot(errormodel_LPD)
summary(errormodel_DI)

expected_error_LPD = terra::predict(AOA$LPD, errormodel_LPD)
plot(expected_error_LPD)

### geodist ~ error
errormodel_geodist = errorProfiles(model, locations=splotdata, variable = "geodist")
plot(errormodel_geodist)
summary(errormodel_DI)

dist <- terra::distance(predictors[[1]],vect(splotdata))
names(dist) <- "geodist"
expected_error_DI <- terra::predict(dist, errormodel_geodist)
plot(expected_error_DI)


### with multiCV = TRUE (for DI ~ error)
errormodel_DI = errorProfiles(model, AOA, multiCV = TRUE, length.out = 3, variable = "DI")
plot(errormodel_DI)

expected_error_DI = terra::predict(AOA$DI, errormodel_DI)
plot(expected_error_DI)

# mask AOA based on new threshold from multiCV
mask_aoa = terra::mask(expected_error_DI, AOA$DI > attr(errormodel_DI, 'AOA_threshold'),
  maskvalues = 1)
plot(mask_aoa)
} # }