The DI is normalized by the DI threshold to allow for a more straightforward interpretation. A value in the resulting DI larger 1 means that the data are more dissimilar than what has been observed during cross-validation. The returned threshold is adjusted accordingly and is, as a consequence, 1.
Examples
if (FALSE) {
library(sf)
library(terra)
library(caret)
# 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)
set.seed(100)
pts <- pts[1:30,]
studyArea <- rast(system.file("extdata","predictors_2012-03-25.tif",package="CAST"))[[1:8]]
trainDat <- extract(studyArea,pts,na.rm=FALSE)
trainDat <- merge(trainDat,pts,by.x="ID",by.y="ID")
# train a model:
set.seed(100)
variables <- c("DEM","NDRE.Sd","TWI")
model <- train(trainDat[,which(names(trainDat)%in%variables)],
trainDat$VW, method="rf", importance=TRUE, tuneLength=1,
trControl=trainControl(method="cv",number=5,savePredictions=T))
#...then calculate the AOA of the trained model for the study area:
AOA <- aoa(studyArea, model)
plot(AOA)
plot(AOA$DI)
#... then normalize the DI
DI_norm <- normalize_DI(AOA)
plot(DI_norm)
plot(DI_norm$DI)
}