Nothing
## ----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))$osl) {
stop("Unexpected vale for Anderson-Darling test")
}
## -----------------------------------------------------------------------------
norm_data %>%
filter(test == "WT" & condition == "RTD") %>%
basis_normal(strength.norm)
## -----------------------------------------------------------------------------
norm_data %>%
filter(test == "WT" & condition == "RTD") %>%
basis_normal(strength.norm,
override = c("outliers_within_batch",
"between_batch_variability"))
## -----------------------------------------------------------------------------
norm_data %>%
filter(test == "WT" & condition == "RTD") %>%
basis_normal(strength.norm, batch)
## -----------------------------------------------------------------------------
norm_data %>%
filter(test == "WT" & condition == "RTD") %>%
ad_ksample(strength.norm, batch)
## -----------------------------------------------------------------------------
norm_data %>%
filter(test == "WT" & condition == "RTD") %>%
group_by(batch) %>%
ggplot(aes(x = strength.norm, color = batch)) +
stat_normal_surv_func() +
stat_esf() +
ggtitle("Distribution of Data For Each Batch")
## -----------------------------------------------------------------------------
norm_data %>%
filter(test == "FC") %>%
group_by(condition, batch) %>%
nest() %>%
mutate(mnr = map(data,
~maximum_normed_residual(data = .x, x = strength.norm)),
tidied = map(mnr, glance)) %>%
select(-c(mnr, data)) %>% # remove unneeded columns
unnest(tidied)
## ----include=FALSE------------------------------------------------------------
if ((norm_data %>%
filter(test == "FC") %>%
group_by(condition, batch) %>%
summarise(
n_outliers = maximum_normed_residual(x = strength.norm)$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)$n_outliers
) %>%
ungroup() %>%
summarise(n_outliers = sum(n_outliers)))[[1]] != 0) {
stop("Unexpected number of outliers")
}
## -----------------------------------------------------------------------------
norm_data %>%
filter(test == "FC") %>%
levene_test(strength.norm, condition)
## ----include=FALSE------------------------------------------------------------
if (!(norm_data %>%
filter(test == "FC") %>%
levene_test(strength.norm, condition))$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)
## -----------------------------------------------------------------------------
norm_data %>%
mutate(condition = ordered(condition,
c("CTD", "RTD", "ETD", "ETW", "ETW2"))) %>%
filter(test == "FC") %>%
basis_pooled_cv(strength.norm, condition, batch)
## -----------------------------------------------------------------------------
carbon.fabric.2 %>%
filter(test == "FC" & condition == "RTD") %>%
equiv_mean_extremum(strength, n_sample = 5, alpha = 0.01)
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