Nothing
## ----setup, echo=FALSE, message=FALSE-----------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
screenshot.force = FALSE,
comment = "#>"
)
library(weibulltools)
## ----dataset_shock, message = FALSE-------------------------------------------
shock_tbl <- reliability_data(data = shock, x = distance, status = status)
shock_tbl
## ---- data_alloy--------------------------------------------------------------
# Data:
alloy_tbl <- reliability_data(data = alloy, x = cycles, status = status)
alloy_tbl
## ----RR_weibull, fig.cap = "Figure 1: RR for a two-parametric Weibull distribution.", message = FALSE----
# rank_regression needs estimated failure probabilities:
shock_cdf <- estimate_cdf(shock_tbl, methods = "johnson")
# Estimating two-parameter Weibull:
rr_weibull <- rank_regression(shock_cdf, distribution = "weibull")
rr_weibull
# Probability plot:
weibull_grid <- plot_prob(
shock_cdf,
distribution = "weibull",
title_main = "Weibull Probability Plot",
title_x = "Mileage in km",
title_y = "Probability of Failure in %",
title_trace = "Defectives",
plot_method = "ggplot2"
)
# Add regression line:
weibull_plot <- plot_mod(
weibull_grid,
x = rr_weibull,
title_trace = "Rank Regression"
)
weibull_plot
## ----ML_weibull, fig.cap = "Figure 2: ML for a two-parametric Weibull distribution.", message = FALSE----
# Again estimating Weibull:
ml_weibull <- ml_estimation(
shock_tbl,
distribution = "weibull"
)
ml_weibull
# Add ML estimation to weibull_grid:
weibull_plot2 <- plot_mod(
weibull_grid,
x = ml_weibull,
title_trace = "Maximum Likelihood"
)
weibull_plot2
## ----ML_estimation_log-normal, message = FALSE--------------------------------
# Two-parameter log-normal:
ml_lognormal <- ml_estimation(
alloy_tbl,
distribution = "lognormal"
)
ml_lognormal
# Three-parameter Log-normal:
ml_lognormal3 <- ml_estimation(
alloy_tbl,
distribution = "lognormal3"
)
ml_lognormal3
## ----ML_visualization_I, fig.cap = "Figure 3: ML for a two-parametric log-normal distribution.", message = FALSE----
# Constructing probability plot:
tbl_cdf_john <- estimate_cdf(alloy_tbl, "johnson")
lognormal_grid <- plot_prob(
tbl_cdf_john,
distribution = "lognormal",
title_main = "Log-normal Probability Plot",
title_x = "Cycles",
title_y = "Probability of Failure in %",
title_trace = "Failed units",
plot_method = "ggplot2"
)
# Add two-parametric model to grid:
lognormal_plot <- plot_mod(
lognormal_grid,
x = ml_lognormal,
title_trace = "Two-parametric log-normal"
)
lognormal_plot
## ----ML_visualization_II, fig.cap = "Figure 4: ML for a three-parametric log-normal distribution.", message = FALSE----
# Add three-parametric model to lognormal_plot:
lognormal3_plot <- plot_mod(
lognormal_grid,
x = ml_lognormal3,
title_trace = "Three-parametric log-normal"
)
lognormal3_plot
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