The DI threshold that separates the area of applicability.

Derived from the CROSS-VALIDATED training data: for each training row, the distance to the nearest training row in a DIFFERENT fold. That set is the dissimilarity the model actually coped with during cross-validation, so the outlier-removed maximum of it is the largest dissimilarity for which the reported error has been demonstrated.

Usage

aoa_threshold(ref, folds, k_iqr = 1.5)

Arguments

ref

From di_reference().

folds

Integer fold label per training row, in the same order as the matrix the reference was built from; NA for a row never held out.

k_iqr

Outlier rule: threshold = Q75 + k_iqr * IQR. 1.5 is Tukey's fence and the value Meyer & Pebesma use.

Value

The threshold, with the cross-validated DI attached as "cv_di" (NA for the rows never held out).

Details

A row never held out – the training side of a single split – has fold NA: it is a neighbour to every fold, and has no cross-validated DI of its own, since no model predicted it.

Examples

set.seed(1)
x_train <- matrix(rnorm(200), 100, 2)    # 100 training rows of 2 scaled predictors
ref <- di_reference(x_train)
th <- aoa_threshold(ref, folds = rep(1:5, 20))
as.numeric(th)                 # the threshold
#> [1] 0.3342118
summary(attr(th, "cv_di"))     # the cross-validated DI it was taken from
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#> 0.02244 0.06969 0.11721 0.13145 0.17550 0.53434