plot_mod: Add Estimated Population Line(s) to a Probability Plot

In weibulltools: Statistical Methods for Life Data Analysis

Add Estimated Population Line(s) to a Probability Plot

Description

This function adds one or multiple estimated regression lines to an existing probability plot (plot_prob). Depending on the output of the functions rank_regression, ml_estimation, mixmod_regression or mixmod_em one or multiple lines are plotted.

Usage

plot_mod(p_obj, x, ...)

## S3 method for class 'wt_model'
plot_mod(p_obj, x, title_trace = "Fit", ...)

Arguments

p_obj	A plot object returned by plot_prob.
x	A list with class wt_model returned by rank_regression, ml_estimation, mixmod_regression or mixmod_em.
...	Further arguments passed to or from other methods. Currently not used.
title_trace	A character string which is assigned to the legend trace.

Details

The name of the legend entry is a combination of the title_trace and the number of determined subgroups from mixmod_regression or mixmod_em. If title_trace = "Line" and the data could be split in two groups, the legend entries would be "Line: 1" and "Line: 2".

Value

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

References

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

Examples

# Reliability data:
data <- reliability_data(data = alloy, x = cycles, status = status)

# Probability estimation:
prob_tbl <- estimate_cdf(data, methods = c("johnson", "kaplan"))


## Rank Regression
# Example 1 - Probability Plot and Regression Line Three-Parameter-Weibull:
plot_weibull <- plot_prob(prob_tbl, distribution = "weibull")
rr_weibull <- rank_regression(prob_tbl, distribution = "weibull3")

plot_reg_weibull <- plot_mod(p_obj = plot_weibull, x = rr_weibull)

# Example 2 - Probability Plot and Regression Line Three-Parameter-Lognormal:
plot_lognormal <- plot_prob(prob_tbl, distribution = "lognormal")
rr_lognormal <- rank_regression(prob_tbl, distribution = "lognormal3")

plot_reg_lognormal <- plot_mod(p_obj = plot_lognormal, x = rr_lognormal)


## ML Estimation
# Example 3 - Probability Plot and Regression Line Two-Parameter-Weibull:
plot_weibull <- plot_prob(prob_tbl, distribution = "weibull")
ml_weibull_2 <- ml_estimation(data, distribution = "weibull")

plot_reg_weibull_2 <- plot_mod(p_obj = plot_weibull, ml_weibull_2)


## Mixture Identification
# Reliability data:
data_mix <- reliability_data(voltage, x = hours, status = status)

# Probability estimation:
prob_mix <- estimate_cdf(
  data_mix,
  methods = c("johnson", "kaplan", "nelson")
)

# Example 4 - Probability Plot and Regression Line Mixmod Regression:
mix_mod_rr <- mixmod_regression(prob_mix, distribution = "weibull")
plot_weibull <- plot_prob(mix_mod_rr)

plot_reg_mix_mod_rr <- plot_mod(p_obj = plot_weibull, x = mix_mod_rr)

# Example 5 - Probability Plot and Regression Line Mixmod EM:
mix_mod_em <- mixmod_em(data_mix)
plot_weibull <- plot_prob(mix_mod_em)

plot_reg_mix_mod_em <- plot_mod(p_obj = plot_weibull, x = mix_mod_em)

weibulltools documentation built on April 5, 2023, 5:10 p.m.