Comments (2)
Nothing, sorry for disturbing you. The problem was the number of events...
> table(u$class)
0 1
47 3
from cutpointr.
Hi @jgarces02,
no worries, it's not a bad question. The error is, as you've concluded correctly, caused by the low number of positives in combination with bootstrapping, because some bootstrap sets didn't contain any positives.
You've also correctly set boot_stratify = TRUE
to avoid that, but that parameter only modifies the "outer" bootstrap, not the bootstrap data sets that are used for cutpoint calculation by maximize_boot_metric
. Honestly, the problem here is that we did not also implement a stratification method for the "inner" bootstrap of maximize_boot_metric
or minimize_boot_metric
.
If the "inner" bootstrap was also stratified, this task should run without any problems. It's of course an edge case with that high class imbalance in combination with the low sample size, but I think we should be able to handle that. I'm going to add this to my to do list.
Best,
Christian
from cutpointr.
Related Issues (20)
- Cutpointr confidence interval predictive positive value HOT 2
- Allow bootstrap stratification for maximize_boot_metric and minimize_boot_metric HOT 1
- Make printing of summary_cutpointr nicer in Rmd documents HOT 1
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- Confidence Intervals for ROC curves
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- Calculating confidence intervals in cutpointr HOT 1
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- direction parameter in the cutpointr() HOT 2
- Set manual color for only one line HOT 3
- add_metric adds the metric column multiple times
- An ambiguous region bounded by two cutpoint
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