This function is used to add estimated confidence region(s) to an existing probability plot. Since confidence regions are related to the estimated regression line, the latter is provided as well.

plot_conf(p_obj, x, ...) # S3 method for wt_confint plot_conf( p_obj, x, title_trace_mod = "Fit", title_trace_conf = "Confidence Limit", ... )

p_obj | A plot object returned by plot_prob. |
---|---|

x | A tibble with class |

... | Further arguments passed to or from other methods. Currently not used. |

title_trace_mod | A character string which is assigned to the model trace in the legend. |

title_trace_conf | A character string which is assigned to the confidence trace in the legend. |

A plot object containing the probability plot with plotting positions, the estimated regression line and the estimated confidence region(s).

Meeker, William Q; Escobar, Luis A., Statistical methods for reliability data, New York: Wiley series in probability and statistics, 1998

# Reliability data: data <- reliability_data(data = alloy, x = cycles, status = status) # Probability estimation: prob_tbl <- estimate_cdf(data, methods = "johnson") # Example 1 - Probability Plot, Regression Line and Confidence Bounds for Three-Parameter-Weibull: rr <- rank_regression(prob_tbl, distribution = "weibull3") conf_betabin <- confint_betabinom(rr) plot_weibull <- plot_prob(prob_tbl, distribution = "weibull") plot_conf_beta <- plot_conf( p_obj = plot_weibull, x = conf_betabin ) # Example 2 - Probability Plot, Regression Line and Confidence Bounds for Three-Parameter-Lognormal: rr_ln <- rank_regression( prob_tbl, distribution = "lognormal3", conf_level = 0.9 ) conf_betabin_ln <- confint_betabinom( rr_ln, bounds = "two_sided", conf_level = 0.9, direction = "y" ) plot_lognormal <- plot_prob(prob_tbl, distribution = "lognormal") plot_conf_beta_ln <- plot_conf( p_obj = plot_lognormal, x = conf_betabin_ln ) # Example 3 - Probability Plot, Regression Line and Confidence Bounds for MLE ml <- ml_estimation(data, distribution = "weibull") conf_fisher <- confint_fisher(ml) plot_weibull <- plot_prob(prob_tbl, distribution = "weibull") plot_conf_fisher_weibull <- plot_conf( p_obj = plot_weibull, x = conf_fisher )