## ----knitr, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----load libraries----------------------------------------------------------- library(ale) library(dplyr) ## ----print var_cars----------------------------------------------------------- print(var_cars) ## ----var_cars summary--------------------------------------------------------- summary(var_cars) ## ----cars_gam----------------------------------------------------------------- cm <- mgcv::gam(mpg ~ cyl + disp + hp + drat + wt + s(qsec) + vs + am + gear + carb + country, data = var_cars) summary(cm) ## ----enable progressr, eval = FALSE------------------------------------------- # # Run this in an R console; it will not work directly within an R Markdown or Quarto block # progressr::handlers(global = TRUE) # progressr::handlers('cli') ## ----cars_ale, fig.width=7, fig.height=14------------------------------------- cars_ale <- ale( var_cars, cm, parallel = 2 # CRAN limit (delete this line on your own computer) ) # Print all plots cars_ale$plots |> patchwork::wrap_plots(ncol = 2) ## ----cars_ale_ixn, fig.width=7, fig.height=7---------------------------------- cars_ale_ixn <- ale_ixn( var_cars, cm, parallel = 2 # CRAN limit (delete this line on your own computer) ) # Print plots cars_ale_ixn$plots |> # extract list of x1 ALE outputs purrr::walk(\(.x1) { # plot all x2 plots in each .x1 element patchwork::wrap_plots(.x1, ncol = 2) |> print() }) ## ----cars_full, fig.width=7, fig.height=14------------------------------------ mb <- model_bootstrap( var_cars, cm, boot_it = 10, # 100 by default but reduced here for a faster demonstration parallel = 2, # CRAN limit (delete this line on your own computer) seed = 2 # workaround to avoid random error on such a small dataset ) mb$ale$plots |> patchwork::wrap_plots(ncol = 2)