Calculate Dissimilarity Index of training data

This function estimates the Dissimilarity Index (DI) within the training data set used for a prediction model. Optionally, the local point density can also be calculated. Predictors can be weighted based on the internal variable importance of the machine learning algorithm used for model training.

Usage

trainDI(
  model = NA,
  train = NULL,
  variables = "all",
  weight = NA,
  CVtest = NULL,
  CVtrain = NULL,
  method = "L2",
  useWeight = TRUE,
  useCV = TRUE,
  LPD = FALSE,
  verbose = TRUE
)

Arguments

model: A train object created with caret used to extract weights from (based on variable importance) as well as cross-validation folds
train: A data.frame containing the data used for model training. Only required when no model is given
variables: character vector of predictor variables. if "all" then all variables of the model are used or if no model is given then of the train dataset.
weight: A data.frame containing weights for each variable. Only required if no model is given.
CVtest: list or vector. Either a list where each element contains the data points used for testing during the cross validation iteration (i.e. held back data). Or a vector that contains the ID of the fold for each training point. Only required if no model is given.
CVtrain: list. Each element contains the data points used for training during the cross validation iteration (i.e. held back data). Only required if no model is given and only required if CVtrain is not the opposite of CVtest (i.e. if a data point is not used for testing, it is used for training). Relevant if some data points are excluded, e.g. when using nndm.
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.
useWeight: Logical. Only if a model is given. Weight variables according to importance in the model?
useCV: Logical. Only if a model is given. Use the CV folds to calculate the DI threshold?
LPD: Logical. Indicates whether the local point density should be calculated or not.
verbose: Logical. Print progress or not?

Value

A list of class trainDI containing:

train: A data frame containing the training data
weight: A data frame with weights based on the variable importance.
variables: Names of the used variables
catvars: Which variables are categorial
scaleparam: Scaling parameters. Output from scale
trainDist_avrg: A data frame with the average distance of each training point to every other point
trainDist_avrgmean: The mean of trainDist_avrg. Used for normalizing the DI
trainDI: Dissimilarity Index of the training data
threshold: The DI threshold used for inside/outside AOA
trainLPD: LPD of the training data
avrgLPD: Average LPD of the training data

Note

This function is called within aoa to estimate the DI and AOA of new data. However, it may also be used on its own if only the DI of training data is of interest, or to facilitate a parallelization of aoa by avoiding a repeated calculation of the DI within the training data.

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

Author

Hanna Meyer, Marvin Ludwig, Fabian Schumacher

Examples

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

# prepare sample data:
data("splotdata")
splotdata = st_drop_geometry(splotdata)

# train a model:
set.seed(100)
model <- caret::train(splotdata[,6:16],
                      splotdata$Species_richness,
                      importance=TRUE, tuneLength=1, ntree = 15, method = "rf",
                      trControl = trainControl(method="cv", number=5, savePredictions=T))
# variable importance is used for scaling predictors
plot(varImp(model,scale=FALSE))

# calculate the DI of the trained model:
DI = trainDI(model=model)
plot(DI)

#...or calculate the DI and LPD of the trained model:
# DI = trainDI(model=model, LPD = TRUE)

# the DI can now be used to compute the AOA (here with LPD):
studyArea = rast(system.file("extdata/predictors_chile.tif", package = "CAST"))
AOA = aoa(studyArea, model = model, trainDI = DI, LPD = TRUE, maxLPD = 1)
print(AOA)
plot(AOA)
plot(AOA$AOA)
plot(AOA$LPD)
} # }