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Hi! I have been excited to start exploring your package, however, unfortunately, my experience with working with it was a bit controversial. After some hours I could fix some implicit data requirements, but I still had some troubles with running auto_MrP. Every time I run the full model I receive this error (however, when using only pcm = TRUE the error is not shown) :
Error in `dplyr::mutate()`:
ℹ In argument: `svm = ...[]`.
Caused by error in `matrix()`:
! length of 'dimnames' [2] not equal to array extent
Run `rlang::last_trace()` to see where the error occurred.
> rlang::last_trace()
<error/dplyr:::mutate_error>
Error in `dplyr::mutate()`:
ℹ In argument: `svm = ...[]`.
Caused by error in `matrix()`:
! length of 'dimnames' [2] not equal to array extent
---
Backtrace:
▆
1. ├─autoMrP::auto_MrP(...)
2. │ └─autoMrP:::run_classifiers(...)
3. │ └─autoMrP:::post_stratification(...)
4. │ └─... %>% ...
5. ├─dplyr::summarize(...)
6. ├─dplyr::group_by(., !!rlang::sym(L2.unit))
7. ├─dplyr::mutate(...)
8. ├─dplyr:::mutate.data.frame(...)
9. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
10. │ ├─base::withCallingHandlers(...)
11. │ └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns)
12. │ └─mask$eval_all_mutate(quo)
13. │ └─dplyr (local) eval()
14. ├─stats::predict(...)
15. └─e1071:::predict.svm(...)
16. ├─stats::napredict(...)
17. ├─stats:::napredict.default(...)
18. └─base::matrix(...)
Run rlang::last_trace(drop = FALSE) to see 3 hidden frames.
Hi! I have been excited to start exploring your package, however, unfortunately, my experience with working with it was a bit controversial. After some hours I could fix some implicit data requirements, but I still had some troubles with running
auto_MrP
. Every time I run the full model I receive this error (however, when using onlypcm = TRUE
the error is not shown) :This is my model:
I am just interested what this error can refer to: problem with model convergence, wrong data properties or it is just a bug? Thanks in advance!
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