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

# If any of the required packages are unavailable,
# don't re-run the code
# nolint start
required <- c("dplyr", "ggplot2", "tidyr", "cmstatr", "purrr")
if (!all(unlist(lapply(required, function(pkg) {
    requireNamespace(pkg, quietly = TRUE)}
  )))) {
  knitr::opts_chunk$set(eval = FALSE)
}
#nolint end

## ----message=FALSE------------------------------------------------------------
library(cmstatr)
library(dplyr)
library(ggplot2)
library(tidyr)
library(purrr)

## -----------------------------------------------------------------------------
carbon.fabric.2 %>%
  head(10)

## -----------------------------------------------------------------------------
norm_data <- carbon.fabric.2 %>%
  filter(test == "WT" | test == "FC") %>%
  mutate(strength.norm = normalize_ply_thickness(strength,
                                                 thickness / nplies,
                                                 0.0079))

norm_data %>%
  head(10)

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "WT" & condition == "RTD") %>%
  anderson_darling_normal(strength.norm)

## ----include=FALSE------------------------------------------------------------
# Verify that the AD test always provides the same conclusion
# If this assertion fails, the Vignette needs to be re-written
if (0.05 >= (norm_data %>%
  filter(test == "WT" & condition == "RTD") %>%
  anderson_darling_normal(strength.norm))$osl) {
  stop("Unexpected vale for Anderson-Darling test")
  }

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "WT" & condition == "RTD") %>%
  basis_normal(strength.norm)

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "WT" & condition == "RTD") %>%
  basis_normal(strength.norm, 
               override = c("outliers_within_batch",
                            "between_batch_variability"))

## -----------------------------------------------------------------------------
b_basis_wt_rtd <- norm_data %>%
  filter(test == "WT" & condition == "RTD") %>%
  basis_normal(strength.norm, batch)

## -----------------------------------------------------------------------------
b_basis_wt_rtd$diagnostic_obj$between_batch_variability

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "WT" & condition == "RTD") %>%
  ad_ksample(strength.norm, batch)

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "WT" & condition == "RTD") %>%
  group_by(batch) %>%
  ggplot(aes(x = strength.norm, color = batch)) +
  stat_normal_surv_func() +
  stat_esf() +
  ggtitle("Distribution of Data For Each Batch")

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "FC") %>%
  group_by(condition, batch) %>%
  nest() %>%
  mutate(mnr = map(data,
                   ~maximum_normed_residual(data = .x, x = strength.norm)),
         tidied = map(mnr, glance)) %>%
  select(-c(mnr, data)) %>%  # remove unneeded columns
  unnest(tidied)

## ----include=FALSE------------------------------------------------------------
if ((norm_data %>%
  filter(test == "FC") %>%
  group_by(condition, batch) %>%
  summarise(
    n_outliers = maximum_normed_residual(x = strength.norm)$n_outliers
    ) %>%
  ungroup() %>%
  summarise(n_outliers = sum(n_outliers)))[[1]] != 0) {
  stop("Unexpected number of outliers")
  }

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "FC") %>%
  group_by(condition) %>%
  nest() %>%
  mutate(adk = map(data, ~ad_ksample(data = .x,
                                     x = strength.norm,
                                     groups = batch)),
         tidied = map(adk, glance)) %>%
  select(-c(data, adk)) %>%  # remove unneeded columns
  unnest(tidied)

## ----include=FALSE------------------------------------------------------------
if (!all(!(norm_data %>%
  filter(test == "FC") %>%
  group_by(condition) %>%
  summarise(different_dist =
           ad_ksample(x = strength.norm, groups = batch)$reject_same_dist
  ))$different_dist)) {
  stop("Unexpected ADK result")
  }

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "FC") %>%
  group_by(condition) %>%
  nest() %>%
  mutate(mnr = map(data, ~maximum_normed_residual(data = .x,
                                                  x = strength.norm)),
         tidied = map(mnr, glance)) %>%
  select(-c(mnr, data)) %>%  # remove unneeded columns
  unnest(tidied)

## ----include=FALSE------------------------------------------------------------
if ((norm_data %>%
  filter(test == "FC") %>%
  group_by(condition) %>%
  summarise(
    n_outliers = maximum_normed_residual(x = strength.norm)$n_outliers
    ) %>%
  ungroup() %>%
  summarise(n_outliers = sum(n_outliers)))[[1]] != 0) {
  stop("Unexpected number of outliers")
  }

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "FC") %>%
  condition_summary(strength.norm, condition, "RTD")

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "FC") %>%
  levene_test(strength.norm, condition)

## ----include=FALSE------------------------------------------------------------
if (!(norm_data %>%
  filter(test == "FC") %>%
  levene_test(strength.norm, condition))$reject_equal_variance) {
  stop("Unexpected result from Levene's test")
  }

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "FC") %>%
  mutate(
    strength_norm_group = normalize_group_mean(strength.norm, condition)) %>%
  levene_test(strength_norm_group, condition)

## ----include=FALSE------------------------------------------------------------
if ((norm_data %>%
  filter(test == "FC") %>%
  mutate(
    strength_norm_group = normalize_group_mean(strength.norm, condition)) %>%
  levene_test(strength_norm_group, condition))$reject_equal_variance) {
  stop("Unexpected value from Levene's test")
  }

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "FC") %>%
  mutate(
    strength_norm_group = normalize_group_mean(strength.norm, condition)) %>%
  anderson_darling_normal(strength_norm_group)

## ----include=FALSE------------------------------------------------------------
if ((norm_data %>%
  filter(test == "FC") %>%
  mutate(
    strength_norm_group = normalize_group_mean(strength.norm, condition)) %>%
  anderson_darling_normal(strength_norm_group))$osl <= 0.05) {
  stop("Unexpected value from AD test")
  }

## -----------------------------------------------------------------------------
norm_data %>%
  filter(test == "FC") %>%
  basis_pooled_cv(strength.norm, condition, batch)

## -----------------------------------------------------------------------------
basis_res <- norm_data %>%
  mutate(condition = ordered(condition,
                             c("CTD", "RTD", "ETD", "ETW", "ETW2"))) %>%
  filter(test == "FC") %>%
  basis_pooled_cv(strength.norm, condition, batch)
basis_res

## -----------------------------------------------------------------------------
basis_res %>%
  condition_summary("RTD")

## -----------------------------------------------------------------------------
carbon.fabric.2 %>%
  filter(test == "FC" & condition == "RTD") %>%
  equiv_mean_extremum(strength, n_sample = 5, alpha = 0.01)