Fit a clinical prediction model — tr_fit • triageR

Fits a binary classification model using the parsnip/workflows framework. The user must specify the model engine explicitly.

Usage

tr_fit(
  data,
  outcome,
  engine = c("logistic_reg", "random_forest", "boost_tree"),
  predictors = NULL
)

Arguments

data: A data frame containing predictors and the outcome column.
outcome: Character. Name of the binary outcome column (must be a factor or coercible to one, with the event of interest as the second level).
engine: Character. Model engine to use. One of "logistic_reg", "random_forest", or "boost_tree".
predictors: Character vector of predictor column names. If NULL (default), all columns except outcome are used.

Value

A fitted triageR_model object — a list containing the fitted workflow, the engine used, and the outcome/predictor names.

Examples

set.seed(1)
df <- data.frame(
  age = round(rnorm(50, 55, 12)),
  sex = sample(c("M", "F"), 50, replace = TRUE),
  disease = sample(c(0, 1), 50, replace = TRUE)
)
model <- tr_fit(df, outcome = "disease", engine = "logistic_reg")
#> Model fitted successfully using engine: logistic_reg