## ----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)

## ----lm_simple, fig.width=7, fig.height=6-------------------------------------

# For speed, these examples use retrieve_rds() to load pre-created objects 
# from an online repository.
# To run the code yourself, execute the code blocks directly.  
serialized_objects_site <- "https://github.com/tripartio/ale/raw/main/download"

ale_lm_attitude_simple <- retrieve_rds(
  # For speed, load a pre-created object by default.
  c(serialized_objects_site, 'ale_lm_attitude_simple.0.5.2.rds'),
  {
    # To run the code yourself, execute this code block directly.
    # For standard models like lm that store their data,
    # there is no need to specify the data argument.
    ALE(lm_attitude)
  }
)
# saveRDS(ale_lm_attitude_simple, file.choose())

# Print all plots
plot(ale_lm_attitude_simple) |> 
  print(ncol = 2)

## ----lm_full_call-------------------------------------------------------------
mb_lm_attitude <- retrieve_rds(
  # For speed, load a pre-created object by default.
  c(serialized_objects_site, 'mb_lm_attitude.0.5.2.rds'),
  {
    # To run the code yourself, execute this code block directly.
    # For standard models like lm that store their data,
    # there is no need to specify the data argument.
    ModelBoot(lm_attitude) # 100 bootstrap iterations by default
  }
)
# saveRDS(mb_lm_attitude, file.choose())

## ----lm_full_stats------------------------------------------------------------
mb_lm_attitude@model_stats

## ----lm_full_coefs------------------------------------------------------------
mb_lm_attitude@model_coefs

## ----lm_full_ale, fig.width=7, fig.height=6-----------------------------------
plot(mb_lm_attitude) |> 
  print(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=6------------------------------------
ale_gam_attitude_simple <- retrieve_rds(
  # For speed, load a pre-created object by default.
  c(serialized_objects_site, 'ale_gam_attitude_simple.0.5.2.rds'),
  {
    # To run the code yourself, execute this code block directly.
    # For standard models like mgcv::gam that store their data,
    # there is no need to specify the data argument.
    ALE(gam_attitude)
  }
)
# saveRDS(ale_gam_attitude_simple, file.choose())

plot(ale_gam_attitude_simple) |> 
  print(ncol = 2)

## ----gam_full_stats-----------------------------------------------------------
mb_gam_attitude <- retrieve_rds(
  # For speed, load a pre-created object by default.
  c(serialized_objects_site, 'mb_gam_attitude.0.5.2.rds'),
  {
    # To run the code yourself, execute this code block directly.
    # For standard models like mgcv::gam that store their data,
    # there is no need to specify the data argument.
    ModelBoot(gam_attitude) # 100 bootstrap iterations by default
  }
)
# saveRDS(mb_gam_attitude, file.choose())

mb_gam_attitude@model_stats

## ----gam_full_coefs-----------------------------------------------------------
mb_gam_attitude@model_coefs

## ----gam_full_ale, fig.width=7, fig.height=6----------------------------------
plot(mb_gam_attitude) |> 
  print(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_attitude_non_standard <- retrieve_rds(
  # For speed, load a pre-created object by default.
  c(serialized_objects_site, 'mb_gam_attitude_non_standard.0.5.2.rds'),
  {
    # To run the code yourself, execute this code block directly.
    # For standard models like mgcv::gam that store their data,
    # there is no need to specify the data argument.
    # 100 bootstrap iterations by default.
    ModelBoot(
      gam_attitude_again,
      model_call_string = 'mgcv::gam(
        rating ~ complaints + privileges + s(learning) +
          raises + s(critical) + advance,
        data = boot_data)'
    )
  }
)
# saveRDS(mb_gam_attitude_non_standard, file.choose())

mb_gam_attitude_non_standard@model_stats