Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
options(rlang_backtrace_on_error = "none")
## ----setup--------------------------------------------------------------------
library(hardhat)
library(modeldata)
data(penguins)
penguins <- na.omit(penguins)
## -----------------------------------------------------------------------------
penguin_form <- mold(body_mass_g ~ log(bill_length_mm), penguins)
names(penguin_form)
## -----------------------------------------------------------------------------
penguin_form$predictors
## -----------------------------------------------------------------------------
penguin_form$outcomes
## -----------------------------------------------------------------------------
mold(body_mass_g ~ log(bill_length_mm) + offset(bill_depth_mm), penguins)$extras
## -----------------------------------------------------------------------------
identical(
mold(~ body_mass_g, penguins),
mold(~ body_mass_g, penguins, blueprint = default_formula_blueprint())
)
## -----------------------------------------------------------------------------
no_intercept <- mold(~ body_mass_g, penguins)
no_intercept$predictors
## -----------------------------------------------------------------------------
with_intercept <- mold(
~ body_mass_g, penguins,
blueprint = default_formula_blueprint(intercept = TRUE)
)
with_intercept$predictors
## ---- error=TRUE--------------------------------------------------------------
mold(~ body_mass_g - 1, penguins)
mold(~ body_mass_g + 0, penguins)
## -----------------------------------------------------------------------------
expanded_dummies <- mold(~ body_mass_g + species, penguins)
expanded_dummies$predictors
## -----------------------------------------------------------------------------
non_expanded_dummies <- mold(
~ body_mass_g + species, penguins,
blueprint = default_formula_blueprint(indicators = "none")
)
non_expanded_dummies$predictors
## -----------------------------------------------------------------------------
k_cols <- mold(~ species, penguins)
k_minus_one_cols <- mold(
~ species, penguins,
blueprint = default_formula_blueprint(intercept = TRUE)
)
colnames(k_cols$predictors)
colnames(k_minus_one_cols$predictors)
## -----------------------------------------------------------------------------
.f <- cbind(body_mass_g, bill_length_mm) ~ bill_depth_mm
frame <- model.frame(.f, penguins)
head(frame)
## -----------------------------------------------------------------------------
ncol(frame)
class(frame$`cbind(body_mass_g, bill_length_mm)`)
head(frame$`cbind(body_mass_g, bill_length_mm)`)
## -----------------------------------------------------------------------------
multivariate <- mold(body_mass_g + log(bill_length_mm) ~ bill_depth_mm, penguins)
multivariate$outcomes
## -----------------------------------------------------------------------------
x <- subset(penguins, select = -body_mass_g)
y <- subset(penguins, select = body_mass_g)
penguin_xy <- mold(x, y)
penguin_xy$predictors
penguin_xy$outcomes
## -----------------------------------------------------------------------------
xy_with_intercept <- mold(x, y, blueprint = default_xy_blueprint(intercept = TRUE))
xy_with_intercept$predictors
## -----------------------------------------------------------------------------
mold(x, y$body_mass_g)$outcomes
## ---- message=FALSE, warning=FALSE--------------------------------------------
library(recipes)
rec <- recipe(bill_length_mm ~ species + bill_depth_mm, penguins) %>%
step_log(bill_length_mm) %>%
step_dummy(species)
penguin_recipe <- mold(rec, penguins)
penguin_recipe$predictors
penguin_recipe$outcomes
## -----------------------------------------------------------------------------
recipe_with_intercept <- mold(
rec, penguins,
blueprint = default_recipe_blueprint(intercept = TRUE)
)
recipe_with_intercept$predictors
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