I am trying to create a linear regression model from openintro::babies that predicts a baby's birthweight from all other variables in the data except case.
I have to following code:
library(tidyverse)
library(tidymodels)
babies <- openintro::babies %>%
drop_na() %>%
mutate(bwt = 28.3495 * bwt) %>%
mutate(weight = 0.453592 * weight)
linear_reg() %>%
set_engine("lm") %>%
fit(formula = bwt ~ ., data = babies %>% select(-case)) %>%
pluck("fit") %>%
augment(babies)
but in my output, I obtain the case variable as well
# A tibble: 1,174 x 14
case bwt gestation parity age height weight smoke .fitted .resid .hat .sigma .cooksd .std.resid
<int> <dbl> <int> <int> <int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 3402. 284 0 27 62 45.4 0 3459. -56.8 0.00374 449. 0.00000863 -0.127
2 2 3203. 282 0 33 64 61.2 0 3547. -344. 0.00227 449. 0.000191 -0.767
3 3 3629. 279 0 28 64 52.2 1 3244. 385. 0.00291 449. 0.000307 0.858
4 5 3062. 282 0 23 67 56.7 1 3396. -334. 0.00475 449. 0.000379 -0.746
5 6 3856. 286 0 25 62 42.2 0 3474. 381. 0.00495 449. 0.000515 0.851
6 7 3912. 244 0 33 62 80.7 0 3065. 848. 0.0137 448. 0.00715 1.90
7 8 3742. 245 0 23 65 63.5 0 3124. 618. 0.00716 449. 0.00197 1.38
8 9 3402. 289 0 25 62 56.7 0 3558. -156. 0.00301 449. 0.0000521 -0.348
9 10 4054. 299 0 30 66 61.7 1 3591. 463. 0.00462 449. 0.000710 1.03
10 11 3969. 351 0 27 68 54.4 0 4527. -558. 0.0221 449. 0.00510 -1.26
# ... with 1,164 more rows
I'm not sure is it the correct way or it is inherent with the output.