to_formula: Create a formula Object

In FSelectorRcpp: 'Rcpp' Implementation of 'FSelector' Entropy-Based Feature Selection Algorithms with a Sparse Matrix Support

Create a formula Object

Description

Utility function to create a formula object. Note that it may be very useful when you use pipes.

Usage

to_formula(attrs, class)

Arguments

attrs	Character vector with names of independent variables.
class	Single string with a dependent variable's name.

Examples

# evaluator from FSelector package
evaluator <- function(subset, data, dependent = names(iris)[5]) {
  library(rpart)
  k <- 5
  splits <- runif(nrow(data))
  results <- sapply(1:k, function(i) {
    test.idx <- (splits >= (i - 1) / k) & (splits < i / k)
    train.idx <- !test.idx
    test <- data[test.idx, , drop = FALSE]
    train <- data[train.idx, , drop = FALSE]
    tree <- rpart(to_formula(subset, dependent), train)
    error.rate <- sum(test[[dependent]] != predict(tree, test, type = "c")) /
    nrow(test)
    return(1 - error.rate)
  })
  return(mean(results))
}

set.seed(123)
fit <- feature_search(attributes = names(iris)[-5], fun = evaluator, data = iris,
                mode = "exhaustive", parallel = FALSE)
fit$best
names(fit$best)[fit$best == 1]
# with to_formula
to_formula(names(fit$best)[fit$best == 1], "Species")

FSelectorRcpp documentation built on April 28, 2023, 5:07 p.m.