## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(featR)

## -----------------------------------------------------------------------------
d <- data.frame(
  spread = c(1, 2, 3, 4, 100, 6, 7, 8),
  flat   = rep(2, 8),
  gappy  = c(1, NA, 3, NA, 5, 6, NA, 8)
)

res <- fs_unsupervised(d, method = "variance", threshold = 1)
res

## -----------------------------------------------------------------------------
selected(res)       # the features that were kept
res$scores          # per-feature scores, comparable within a method
res$method          # which method produced this
res$task            # "classification", "regression", or NA
names(res$details)  # everything method-specific

## -----------------------------------------------------------------------------
summary(res)

## -----------------------------------------------------------------------------
train <- data.frame(
  strong = c(1, 2, 3, 4, 5, 6),
  mirror = c(6, 5, 4, 3, 2, 1),
  noise  = c(1, 0, 1, 0, 1, 0),
  y      = c(1, 2, 3, 4, 5, 6)
)

fs_supervised(train, target = "y", threshold = 0.9)

## -----------------------------------------------------------------------------
fs_correlation(train[, c("strong", "mirror", "noise")], threshold = 0.9)

## -----------------------------------------------------------------------------
ig <- data.frame(
  perfect = rep(c("a", "b", "c", "c"), 5),
  half    = rep(c("n1", "n2"), 10),
  target  = factor(rep(c("a", "b", "c", "c"), 5))
)

fs_infogain(ig, target = "target")$scores