recipe_roles: Set Recipe Roles

In MachineShop: Machine Learning Models and Tools

Set Recipe Roles

Description

Add to or replace the roles of variables in a preprocessing recipe.

Usage

role_binom(recipe, x, size)

role_case(recipe, group, stratum, weight, replace = FALSE)

role_pred(recipe, offset, replace = FALSE)

role_surv(recipe, time, event)

Arguments

recipe	existing recipe object.
x, size	number of counts and trials for the specification of a BinomialVariate outcome.
group	variable defining groupings of case observations, such as repeated measurements, to keep together during resampling [default: none].
stratum	variable to use in conducting stratified resample estimation of model performance.
weight	numeric variable of case weights for model fitting.
replace	logical indicating whether to replace existing roles.
offset	numeric variable to be added to a linear predictor, such as in a generalized linear model, with known coefficient 1 rather than an estimated coefficient.
time, event	numeric follow up time and 0-1 numeric or logical event indicator for specification of a Surv outcome. If the event indicator is omitted, all cases are assumed to have events.

Value

An updated recipe object.

See Also

recipe

Examples

library(survival)
library(recipes)

df <- within(veteran, {
  y <- Surv(time, status)
  remove(time, status)
})
rec <- recipe(y ~ ., data = df) %>%
  role_case(stratum = y)

(res <- resample(rec, model = CoxModel))
summary(res)

MachineShop documentation built on Sept. 18, 2023, 5:06 p.m.