tests/testthat/_snaps/lencode_bayes.md

In topepo/embed: Extra Recipes for Encoding Predictors

factor encoded predictor

Code
  class_test <- recipe(x2 ~ ., data = ex_dat) %>% step_lencode_bayes(x3, outcome = vars(
    x2), verbose = FALSE, options = opts) %>% prep(training = ex_dat, retain = TRUE)
Condition

Code
  new_values_ch <- bake(class_test, new_data = new_dat_ch)
Condition
  Warning:
  ! There was 1 column that was a factor when the recipe was prepped:
  * `x3`
  i This may cause errors when processing new data.

Code
  set.seed(8283)
  reg_test <- recipe(x1 ~ ., data = ex_dat) %>% step_lencode_bayes(x3, outcome = vars(
    x1), verbose = FALSE, options = opts) %>% prep(training = ex_dat, retain = TRUE)
Condition

Code
  new_values_ch <- bake(reg_test, new_data = new_dat_ch)
Condition
  Warning:
  ! There was 1 column that was a factor when the recipe was prepped:
  * `x3`
  i This may cause errors when processing new data.

character encoded predictor

Code
  class_test <- recipe(x2 ~ ., data = ex_dat_ch) %>% step_lencode_bayes(x3,
    outcome = vars(x2), verbose = FALSE, options = opts, id = "id") %>% prep(
    training = ex_dat_ch, retain = TRUE, options = opts)
Condition

Code
  set.seed(8283)
  reg_test <- recipe(x1 ~ ., data = ex_dat_ch) %>% step_lencode_bayes(x3,
    outcome = vars(x1), verbose = FALSE, options = opts) %>% prep(training = ex_dat_ch,
    retain = TRUE)
Condition

Works with passing family

Code
  class_test <- recipe(outcome ~ ., data = ex_dat_poisson) %>% step_lencode_bayes(
    x3, outcome = vars(outcome), verbose = FALSE, options = c(opts, family = stats::poisson)) %>%
    prep(training = ex_dat_poisson, retain = TRUE)
Condition

Code
  new_values_ch <- bake(class_test, new_data = new_dat_ch)
Condition
  Warning:
  ! There was 1 column that was a factor when the recipe was prepped:
  * `x3`
  i This may cause errors when processing new data.

case weights

Code
  class_test <- recipe(x2 ~ ., data = ex_dat_cw) %>% step_lencode_bayes(x3,
    outcome = vars(x2), verbose = FALSE, options = opts) %>% prep(training = ex_dat_cw,
    retain = TRUE)
Condition
Code
  junk <- capture.output(ref_mod <- rstanarm::stan_glmer(formula = x2 ~ (1 |
  value), data = ex_dat_cw %>% transmute(value = x3, x2), family = binomial(),
  na.action = na.omit, seed = 34677, chains = 2, iter = 500, weights = wts_int, ))
Condition

Code
  class_test
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:      1
  predictor:    3
  case_weights: 1

  -- Training information 
  Training data contained 500 data points and no incomplete rows.

  -- Operations 
  * Linear embedding for factors via Bayesian GLM for: x3 | Trained, weighted

empty printing

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Operations 
  * Linear embedding for factors via Bayesian GLM for: <none>

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Training information 
  Training data contained 32 data points and no incomplete rows.

  -- Operations 
  * Linear embedding for factors via Bayesian GLM for: <none> | Trained

printing

Code
  print(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 3

  -- Operations 
  * Linear embedding for factors via Bayesian GLM for: x3

Code
  prep(rec)
Condition
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 3

  -- Training information 
  Training data contained 500 data points and no incomplete rows.

  -- Operations 
  * Linear embedding for factors via Bayesian GLM for: x3 | Trained

topepo/embed documentation built on March 26, 2024, 4:11 a.m.