Comments (2)
Perfect! I didn't know a so easy way! Thanks a lot!
from cutpointr.
Hi,
to make the for-loop work, you should use !!
so that the i
gets evaluated not as i
but as the character strings:
for(i in c("dsi", "age")) {
print(cutpointr(data = suicide, x = !!i, class = suicide))
}
In this application, maybe multi_cutpointr
could be useful. It returns a tibble where the rows are the cutpoints per predictor / subgroup:
library(cutpointr)
multi_cutpointr(suicide, x = c("dsi", "age"), class = suicide, metric = sum_sens_spec, pos_class = "yes")
#> dsi:
#> Assuming the positive class has higher x values
#> age:
#> Assuming the positive class has lower x values
#> # A tibble: 2 x 16
#> direction optimal_cutpoint method sum_sens_spec acc sensitivity
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 >= 2 maximize_metric 1.75179 0.864662 0.888889
#> 2 <= 55 maximize_metric 1.11537 0.199248 0.972222
#> specificity AUC pos_class neg_class prevalence outcome predictor
#> <dbl> <dbl> <chr> <fct> <dbl> <chr> <chr>
#> 1 0.862903 0.923779 yes no 0.0676692 suicide dsi
#> 2 0.143145 0.525678 yes no 0.0676692 suicide age
#> data roc_curve boot
#> <list> <list> <lgl>
#> 1 <tibble [532 x 2]> <tibble [13 x 10]> NA
#> 2 <tibble [532 x 2]> <tibble [61 x 10]> NA
Created on 2020-07-10 by the reprex package (v0.3.0)
from cutpointr.
Related Issues (20)
- Cutpointr confidence interval predictive positive value HOT 2
- Missing metrics if maximize/minimize_boot_metric HOT 2
- Allow bootstrap stratification for maximize_boot_metric and minimize_boot_metric HOT 1
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from cutpointr.