tests/testthat/_snaps/check_enough_train_data.md

In cmu-delphi/epipredict: Basic epidemiology forecasting methods

check_enough_train_data works on pooled data

Code
  epi_recipe(toy_epi_df) %>% check_enough_train_data(x, y, n = 2 * n + 1,
  drop_na = FALSE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
Condition
  Error in `prep()`:
  ! The following columns don't have enough data to predict: x and y.

Code
  epi_recipe(toy_epi_df) %>% check_enough_train_data(x, y, n = 2 * n - 1,
  drop_na = TRUE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
Condition
  Error in `prep()`:
  ! The following columns don't have enough data to predict: x and y.

check_enough_train_data works on unpooled data

Code
  epi_recipe(toy_epi_df) %>% check_enough_train_data(x, y, n = n + 1, epi_keys = "geo_value",
  drop_na = FALSE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
Condition
  Error in `prep()`:
  ! The following columns don't have enough data to predict: x and y.

Code
  epi_recipe(toy_epi_df) %>% check_enough_train_data(x, y, n = 2 * n - 3,
  epi_keys = "geo_value", drop_na = TRUE) %>% prep(toy_epi_df) %>% bake(new_data = NULL)
Condition
  Error in `prep()`:
  ! The following columns don't have enough data to predict: x and y.

check_enough_train_data works with all_predictors() downstream of constructed terms

Code
  epi_recipe(toy_epi_df) %>% step_epi_lag(x, lag = c(1, 2)) %>%
    check_enough_train_data(all_predictors(), y, n = 2 * n - 5) %>% prep(
    toy_epi_df) %>% bake(new_data = NULL)
Condition
  Error in `prep()`:
  ! The following columns don't have enough data to predict: lag_1_x, lag_2_x, and y.

cmu-delphi/epipredict documentation built on March 5, 2025, 12:17 p.m.