Nothing
## ----setup, echo=FALSE, message=FALSE-----------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
screenshot.force = FALSE,
comment = "#>"
)
library(weibulltools)
## ----rank_densities, fig.cap = "Figure 1: Densities for different ranks i in samples of size n = 10.", message = FALSE, warning = FALSE----
library(dplyr) # data manipulation
library(ggplot2) # visualization
x <- seq(0, 1, length.out = 100) # CDF
n <- 10 # sample size
i <- c(1, 3, 5, 7, 9) # ranks
r <- n - i + 1 # inverse ranking
df_dens <- expand.grid(cdf = x, i = i) %>%
mutate(n = n, r = n - i + 1, pdf = dbeta(x = x, shape1 = i, shape2 = r))
densplot <- ggplot(data = df_dens, aes(x = cdf, y = pdf, colour = as.factor(i))) +
geom_line() +
scale_colour_discrete(guide = guide_legend(title = "i")) +
theme_bw() +
labs(x = "Failure Probability", y = "Density")
densplot
## ----dataset_shock, message = FALSE-------------------------------------------
shock_tbl <- reliability_data(data = shock, x = distance, status = status)
shock_tbl
## ----failure_probabilities----------------------------------------------------
# Estimate CDF with both methods:
cdf_tbl <- estimate_cdf(shock_tbl, methods = c("mr", "johnson"))
# First case where only failed units are taken into account:
cdf_tbl_mr <- cdf_tbl %>% filter(cdf_estimation_method == "mr")
cdf_tbl_mr
# Second case where both, survived and failed units are considered:
cdf_tbl_john <- cdf_tbl %>% filter(cdf_estimation_method == "johnson")
cdf_tbl_john
## ----probability_plot_weibull, fig.cap = "Figure 3: Plotting positions in Weibull grid.", message = FALSE----
# Weibull grid for estimated probabilities:
weibull_grid <- plot_prob(
cdf_tbl,
distribution = "weibull",
title_main = "Weibull Probability Plot",
title_x = "Mileage in km",
title_y = "Probability of Failure in %",
title_trace = "Method",
plot_method = "ggplot2"
)
weibull_grid
## ----probability_plot_log-normal, fig.cap = "Figure 4: Plotting positions in log-normal grid.", message = FALSE----
# Log-normal grid for estimated probabilities:
lognorm_grid <- plot_prob(
cdf_tbl,
distribution = "lognormal",
title_main = "Log-normal Probability Plot",
title_x = "Mileage in km",
title_y = "Probability of Failure in %",
title_trace = "Method",
plot_method = "ggplot2"
)
lognorm_grid
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