The documentation suggests that it may be possible to perform leave one out cross validation (LOOCV) with create.data.split by setting num.folds equal to n, where n is the total number of samples. While the verbose output of the function claims that this is what is happening, the function actually creates n-1 folds and always puts two samples in the first test fold.
Additionally (though I'm not sure how related it is), the siamcat object produced by attempting LOOCV with num.folds = n in create.data.split fails in make.predictions with the following error message:
Error in make.predictions(siamcat) :
nrow(data) == length(test.label) is not TRUE
These issues are reproducible using the data and procedure from the SIAMCAT basic vignette.
The documentation suggests that it may be possible to perform leave one out cross validation (LOOCV) with create.data.split by setting num.folds equal to n, where n is the total number of samples. While the verbose output of the function claims that this is what is happening, the function actually creates n-1 folds and always puts two samples in the first test fold.
Additionally (though I'm not sure how related it is), the siamcat object produced by attempting LOOCV with num.folds = n in create.data.split fails in make.predictions with the following error message:
Error in make.predictions(siamcat) : nrow(data) == length(test.label) is not TRUE
These issues are reproducible using the data and procedure from the SIAMCAT basic vignette.