## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
has_rf <- requireNamespace("randomForest", quietly = TRUE)

## -----------------------------------------------------------------------------
library(rankimp)

judges <- rbind(
  permutation_seed1 = c(1, 2, 3, 4, 5),
  permutation_seed2 = c(1, 2, 3, 5, 4),
  permutation_seed3 = c(2, 1, 3, 4, 5),
  shap              = c(1, 3, 2, 4, 5),
  impurity          = c(1, 2, 4, 3, 5),
  loco              = c(2, 1, 3, 5, 4)
)
colnames(judges) <- c("income", "age", "balance", "region", "tenure")

cr <- consensus_rank(judges)
cr

## -----------------------------------------------------------------------------
item_consensus(cr)

## -----------------------------------------------------------------------------
set.seed(1)
cb <- rank_confsets(cr, n_boot = 500)
cb

## ----fig.width = 6, fig.height = 3.5------------------------------------------
autoplot(cb)

## -----------------------------------------------------------------------------
prob_topk(cb, k = 2)

## -----------------------------------------------------------------------------
rank_select(cb, threshold = 3)

## ----eval = has_rf------------------------------------------------------------
set.seed(7)
n <- 80
sim <- as.data.frame(matrix(rnorm(n * 8), n, 8))
names(sim) <- paste0("x", 1:8)
sim$y <- 2 * sim$x1 + 0.60 * sim$x2 + 0.55 * sim$x3 +
  0.50 * sim$x4 + 0.45 * sim$x5 + rnorm(n, sd = 1)

set.seed(1)
forest <- randomForest::randomForest(y ~ ., data = sim, ntree = 200)

set.seed(2)
sim_panel <- importance_judges(forest, methods = c("permutation", "mdi"),
                               data = sim, target = "y", seeds = 1:3)
cr_sim <- consensus_rank(sim_panel)
cr_sim

## ----eval = has_rf------------------------------------------------------------
set.seed(3)
by_judges <- rank_confsets(cr_sim, n_boot = 500)

set.seed(3)
by_data <- rank_confsets(cr_sim, type = "data")

by_data

## ----eval = has_rf------------------------------------------------------------
rank_select(by_judges, threshold = 3)
rank_select(by_data, threshold = 3)