Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 6
)
# If any of the required packages are unavailable,
# don't re-run the code
# nolint start
required <- c("dplyr", "ggplot2", "tidyr", "cmstatr")
if (!all(unlist(lapply(required, function(pkg) {
requireNamespace(pkg, quietly = TRUE)}
)))) {
knitr::opts_chunk$set(eval = FALSE)
}
# nolint end
## ----message=FALSE------------------------------------------------------------
library(dplyr)
library(ggplot2)
library(tidyr)
library(cmstatr)
## -----------------------------------------------------------------------------
dat <- carbon.fabric.2 %>%
filter(test == "WT") %>%
mutate(condition = ordered(condition, c("CTD", "RTD", "ETW", "ETW2")))
dat %>%
head(10)
## -----------------------------------------------------------------------------
b_basis_pooled <- dat %>%
basis_pooled_cv(strength, condition, batch,
override = c("between_group_variability",
"normalized_variance_equal"))
b_basis_pooled
## -----------------------------------------------------------------------------
b_basis_pooled$basis
## -----------------------------------------------------------------------------
dat %>%
ggplot(aes(x = batch, y = strength)) +
geom_boxplot() +
geom_jitter(width = 0.25) +
geom_hline(aes(yintercept = value),
data = b_basis_pooled$basis %>% rename(condition = group),
color = "blue") +
facet_grid(. ~ condition) +
theme_bw() +
ggtitle("Batch Plot")
## -----------------------------------------------------------------------------
dat %>%
ggplot(aes(x = strength, color = condition)) +
stat_ecdf(geom = "point") +
coord_flip() +
theme_bw() +
ggtitle("Quantile Plot")
## -----------------------------------------------------------------------------
dat %>%
ggplot(aes(x = strength, color = condition)) +
stat_normal_surv_func() +
stat_esf() +
theme_bw() +
ggtitle("Normal Survival Function Plot")
## -----------------------------------------------------------------------------
dat %>%
group_by(condition) %>%
mutate(norm.score = scale(strength)) %>%
ggplot(aes(x = norm.score, y = strength, colour = condition)) +
geom_point() +
ggtitle("Normal Scores Plot") +
theme_bw()
## -----------------------------------------------------------------------------
dat %>%
ggplot(aes(sample = strength, colour = condition)) +
geom_qq() +
geom_qq_line() +
ggtitle("Q-Q Plot") +
theme_bw()
## -----------------------------------------------------------------------------
b_basis_fcn <- tribble(
~condition, ~fcn, ~args,
"CTD", "basis_normal", list(override = c("between_batch_variability")),
"RTD", "basis_normal", list(override = c("between_batch_variability")),
"ETW", "basis_hk_ext", NULL,
"ETW2", "basis_normal", list(override = c("between_batch_variability"))
)
a_basis_fcn <- tribble(
~condition, ~fcn, ~args,
"CTD", "basis_normal", list(override = c("between_batch_variability")),
"RTD", "basis_normal", list(override = c("between_batch_variability")),
"ETW", "basis_hk_ext", list(method = "woodward-frawley"),
"ETW2", "basis_normal", list(override = c("between_batch_variability"))
)
## -----------------------------------------------------------------------------
single_point_fcn <- function(group_x, group_batch, cond, basis_fcn, p) {
fcn <- basis_fcn$fcn[basis_fcn$condition == cond[1]]
extra_args <- basis_fcn$args[basis_fcn$condition == cond[1]]
args <- c(
list(x = group_x, batch = group_batch, p = p),
unlist(extra_args))
basis <- do.call(fcn, args)
basis$basis
}
single_point_results <- dat %>%
group_by(condition) %>%
summarise(single_point_b_basis = single_point_fcn(
strength, batch, condition, b_basis_fcn, 0.90),
single_point_a_basis = single_point_fcn(
strength, batch, condition, a_basis_fcn, 0.99),
minimum = min(strength),
mean = mean(strength)) %>%
mutate(condition = ordered(condition, c("CTD", "RTD", "ETW", "ETW2")))
single_point_results
## -----------------------------------------------------------------------------
a_basis_pooled <- dat %>%
basis_pooled_cv(strength, condition, batch, p = 0.99,
override = c("between_group_variability",
"normalized_variance_equal"))
a_basis_pooled
## -----------------------------------------------------------------------------
a_basis_pooled$basis
## -----------------------------------------------------------------------------
a_basis_pooled$basis %>%
rename(condition = group,
b_basis_pooled = value)
## -----------------------------------------------------------------------------
a_basis_pooled_results <- a_basis_pooled$basis %>%
rename(condition = group,
a_basis_pooled = value) %>%
mutate(condition = ordered(condition, c("CTD", "RTD", "ETW", "ETW2")))
a_basis_pooled_results
## -----------------------------------------------------------------------------
b_basis_pooled_results <- b_basis_pooled$basis %>%
rename(condition = group,
b_basis_pooled = value) %>%
mutate(condition = ordered(condition, c("CTD", "RTD", "ETW", "ETW2")))
b_basis_pooled_results
## -----------------------------------------------------------------------------
single_point_results %>%
inner_join(b_basis_pooled_results, by = "condition") %>%
inner_join(a_basis_pooled_results, by = "condition")
## -----------------------------------------------------------------------------
single_point_results %>%
inner_join(b_basis_pooled_results, by = "condition") %>%
inner_join(a_basis_pooled_results, by = "condition") %>%
pivot_longer(cols = single_point_b_basis:a_basis_pooled)
## -----------------------------------------------------------------------------
single_point_results %>%
inner_join(b_basis_pooled_results, by = "condition") %>%
inner_join(a_basis_pooled_results, by = "condition") %>%
pivot_longer(cols = single_point_b_basis:a_basis_pooled) %>%
ggplot(aes(x = condition, y = value)) +
geom_boxplot(aes(y = strength), data = dat) +
geom_point(aes(shape = name, color = name)) +
ggtitle("Property Graph") +
theme_bw()
## -----------------------------------------------------------------------------
carbon.fabric.2 %>%
mutate(panel = as.character(panel)) %>%
filter(test == "WT") %>%
nested_data_plot(strength,
groups = c(batch, panel))
## -----------------------------------------------------------------------------
carbon.fabric.2 %>%
mutate(panel = as.character(panel)) %>%
filter(test == "WT" & condition == "RTD") %>%
nested_data_plot(strength,
groups = c(batch, panel),
fill = batch,
color = panel)
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