## ----knitr, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----load libraries----------------------------------------------------------- library(ale) ## ----attitude_str------------------------------------------------------------- str(attitude) ## ----attitude_summary--------------------------------------------------------- summary(attitude) ## ----lm_summary--------------------------------------------------------------- lm_attitude <- lm(rating ~ ., data = attitude) summary(lm_attitude) ## ----enable progressr, eval = FALSE------------------------------------------- # # Run this in an R console; it will not work directly within an R Markdown or Quarto block # progressr::handlers(global = TRUE) # progressr::handlers('cli') ## ----lm_simple, fig.width=7, fig.height=7------------------------------------- ale_lm_attitude_simple <- ale( attitude, lm_attitude, parallel = 2 # CRAN limit (delete this line on your own computer) ) # Print all plots ale_lm_attitude_simple$plots |> patchwork::wrap_plots(ncol = 2) ## ----lm_ixn, fig.width=7, fig.height=7---------------------------------------- ale_lm_attitude_ixn <- ale_ixn( attitude, lm_attitude, parallel = 2 # CRAN limit (delete this line on your own computer) ) # Print plots ale_lm_attitude_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() }) ## ----lm_full_call------------------------------------------------------------- mb_lm <- model_bootstrap( attitude, lm_attitude, boot_it = 10, # 100 by default but reduced here for a faster demonstration parallel = 2 # CRAN limit (delete this line on your own computer) ) ## ----lm_full_stats------------------------------------------------------------ mb_lm$model_stats ## ----lm_full_coefs------------------------------------------------------------ mb_lm$model_coefs ## ----lm_full_ale, fig.width=7, fig.height=7----------------------------------- mb_lm$ale$plots |> patchwork::wrap_plots(ncol = 2) ## ----gam_summary-------------------------------------------------------------- gam_attitude <- mgcv::gam(rating ~ complaints + privileges + s(learning) + raises + s(critical) + advance, data = attitude) summary(gam_attitude) ## ----gam_simple, fig.width=7, fig.height=7------------------------------------ ale_gam_attitude_simple <- ale( attitude, gam_attitude, parallel = 2 # CRAN limit (delete this line on your own computer) ) ale_gam_attitude_simple$plots |> patchwork::wrap_plots(ncol = 2) ## ----gam_full_stats----------------------------------------------------------- mb_gam <- model_bootstrap( attitude, gam_attitude, boot_it = 10, # 100 by default but reduced here for a faster demonstration parallel = 2 # CRAN limit (delete this line on your own computer) ) mb_gam$model_stats ## ----gam_full_coefs----------------------------------------------------------- mb_gam$model_coefs ## ----gam_full_ale, fig.width=7, fig.height=7---------------------------------- mb_gam$ale$plots |> patchwork::wrap_plots(ncol = 2) ## ----gam_summary_repeat------------------------------------------------------- gam_attitude_again <- mgcv::gam(rating ~ complaints + privileges + s(learning) + raises + s(critical) + advance, data = attitude) summary(gam_attitude_again) ## ----model_call_string-------------------------------------------------------- mb_gam_non_standard <- model_bootstrap( attitude, gam_attitude_again, model_call_string = 'mgcv::gam(rating ~ complaints + privileges + s(learning) + raises + s(critical) + advance, data = boot_data)', boot_it = 10, # 100 by default but reduced here for a faster demonstration parallel = 2 # CRAN limit (delete this line on your own computer) ) mb_gam_non_standard$model_stats