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.
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
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.
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
Code
bake(rec_trained, new_data = ex_dat[, -3])
Condition
Error in `step_lencode_bayes()`:
! The following required column is missing from `new_data`: x3.
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
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
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