tidysynthesis (version 0.1.2)

sample_rpart: Sample the conditional distribution created by a CART model

Description

Sample the conditional distribution created by a CART model

Usage

sample_rpart(model, new_data, conf_data, ignore_zeros = TRUE)

Value

A numeric vector of predictions

Arguments

model

A "model_fit" object created by rpart

new_data

A data frame with predictors

conf_data

A data frame with original confidential predictors

ignore_zeros

Should a vector of all 0 observations return NA for the l-diversity calculation. Defaults to TRUE.

Examples

rpart_mod_reg <- parsnip::decision_tree() |>
  parsnip::set_engine("rpart") |>
  parsnip::set_mode(mode = "regression")

 regression_rec <- recipes::recipe(inctot ~ ., data = acs_conf)
 
 model_reg <- workflows::workflow() |>
   workflows::add_model(spec = rpart_mod_reg) |>
   workflows::add_recipe(recipe = regression_rec) |>
   parsnip::fit(data = acs_conf)
 
 set.seed(1)
 sample1 <- sample_rpart(
   model = model_reg, 
   new_data = acs_conf[1:3, ], 
   conf_data = acs_conf
 )
 

rpart_mod_class <- parsnip::decision_tree() |>
  parsnip::set_engine("rpart") |>
  parsnip::set_mode(mode = "classification")

classification_rec <- recipes::recipe(hcovany ~ ., data = acs_conf)

model_reg <- workflows::workflow() |>
  workflows::add_model(spec = rpart_mod_class) |>
  workflows::add_recipe(recipe = classification_rec) |>
  parsnip::fit(data = acs_conf)

set.seed(1)
sample1 <- sample_rpart(
  model = model_reg, 
  new_data = acs_conf[1:10, ], 
  conf_data = acs_conf
)