plot_prob: Probability Plotting Method for Univariate Lifetime...

In weibulltools: Statistical Methods for Life Data Analysis

Probability Plotting Method for Univariate Lifetime Distributions

Description

This function is used to apply the graphical technique of probability plotting. It is either applied to the output of estimate_cdf (plot_prob.wt_cdf_estimation) or to the output of a mixture model from mixmod_regression / mixmod_em (plot_prob.wt_model). Note that in the latter case no distribution has to be specified because it is inferred from the model.

Usage

plot_prob(x, ...)

## S3 method for class 'wt_cdf_estimation'
plot_prob(
  x,
  distribution = c("weibull", "lognormal", "loglogistic", "sev", "normal", "logistic",
    "exponential"),
  title_main = "Probability Plot",
  title_x = "Characteristic",
  title_y = "Unreliability",
  title_trace = "Sample",
  plot_method = c("plotly", "ggplot2"),
  ...
)

## S3 method for class 'wt_model'
plot_prob(
  x,
  title_main = "Probability Plot",
  title_x = "Characteristic",
  title_y = "Unreliability",
  title_trace = "Sample",
  plot_method = c("plotly", "ggplot2"),
  ...
)

Arguments

x	A tibble with class wt_cdf_estimation returned by estimate_cdf or 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.
distribution	Supposed distribution of the random variable.
title_main	A character string which is assigned to the main title.
title_x	A character string which is assigned to the title of the x axis.
title_y	A character string which is assigned to the title of the y axis.
title_trace	A character string which is assigned to the legend trace.
plot_method	Package, which is used for generating the plot output.

Details

If x was split by mixmod_em, estimate_cdf with method "johnson" is applied to subgroup-specific data. The calculated plotting positions are shaped according to the determined split in mixmod_em.

In mixmod_regression a maximum of three subgroups can be determined and thus being plotted. The intention of this function is to give the user a hint for the existence of a mixture model. An in-depth analysis should be done afterwards.

For plot_method == "plotly" the marker label for x and y are determined by the first word provided in the argument title_x and title_y respectively, i.e. if title_x = "Mileage in km" the x label of the marker is "Mileage". The name of the legend entry is a combination of the title_trace and the number of determined subgroups (if any). If title_trace = "Group" and the data has been split in two groups, the legend entries are "Group: 1" and "Group: 2".

Value

A plot object containing the probability plot.

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(
  alloy,
  x = cycles,
  status = status
)

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

# Example 1 - Probability Plot Weibull:
plot_weibull <- plot_prob(prob_tbl)

# Example 2 - Probability Plot Lognormal:
plot_lognormal <- plot_prob(
  x = prob_tbl,
  distribution = "lognormal"
)

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

prob_mix <- estimate_cdf(
  data_mix,
  methods = c("johnson", "kaplan")
)

# Example 3 - Mixture identification using mixmod_regression:
mix_mod_rr <- mixmod_regression(prob_mix)

plot_mix_mod_rr <- plot_prob(x = mix_mod_rr)

# Example 4 - Mixture identification using mixmod_em:
mix_mod_em <- mixmod_em(data_mix)

plot_mix_mod_em <- plot_prob(x = mix_mod_em)

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