wrong structure for logistic regression confidence intervals

[topepo]

library(tidymodels)
#> ── Attaching packages ───────────────────────────────────────────────────────────────── tidymodels 0.0.2 ──
#> ✔ broom     0.5.1          ✔ purrr     0.3.0     
#> ✔ dials     0.0.2          ✔ recipes   0.1.4     
#> ✔ dplyr     0.8.0.1        ✔ rsample   0.0.4     
#> ✔ ggplot2   3.1.0          ✔ tibble    2.0.1     
#> ✔ infer     0.4.0          ✔ yardstick 0.0.2     
#> ✔ parsnip   0.0.1.9000
#> ── Conflicts ──────────────────────────────────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter()  masks stats::filter()
#> ✖ dplyr::lag()     masks stats::lag()
#> ✖ recipes::step()  masks stats::step()

data("lending_club")

lending_club <-
  lending_club %>%
  slice(1:200) 

log_reg <-
  logistic_reg() %>%
  set_engine("glm") %>%
  fit(Class ~ log(funded_amnt) + int_rate, data = lending_club)

predict(log_reg, lending_club %>% slice(1:7) %>% dplyr::select(-Class), type = "prob")
#> # A tibble: 7 x 2
#>   .pred_bad .pred_good
#>       <dbl>      <dbl>
#> 1   0.0511       0.949
#> 2   0.0706       0.929
#> 3   0.0500       0.950
#> 4   0.0499       0.950
#> 5   0.00185      0.998
#> 6   0.0166       0.983
#> 7   0.00441      0.996

predict(log_reg, lending_club %>% slice(1:7) %>% dplyr::select(-Class), type = "conf_int")
#> # A tibble: 7 x 2
#>   .pred_lower .pred_upper
#>         <dbl>       <dbl>
#> 1       0.896       0.976
#> 2       0.813       0.975
#> 3       0.883       0.980
#> 4       0.897       0.976
#> 5       0.975       1.000
#> 6       0.944       0.995
#> 7       0.971       0.999

Created on 2019-02-25 by the reprex package (v0.2.1)

[topepo]

same for stan engine too

[github-actions]

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