weibulltools (version 2.1.0)

plot_prob.default: Probability Plotting Method for Univariate Lifetime Distributions

Description

This function is used to apply the graphical technique of probability plotting.

Usage

# S3 method for default
plot_prob(
  x,
  y,
  status,
  id = rep("XXXXXX", length(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"),
  ...
)

Value

A plot object containing the probability plot.

Arguments

x

A numeric vector which consists of lifetime data. Lifetime data could be every characteristic influencing the reliability of a product, e.g. operating time (days/months in service), mileage (km, miles), load cycles.

y

A numeric vector which consists of estimated failure probabilities regarding the lifetime data in x.

status

A vector of binary data (0 or 1) indicating whether a unit is a right censored observation (= 0) or a failure (= 1).

id

Identification for every unit.

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.

...

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

Details

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".

References

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

See Also

plot_prob

Examples

# Vectors:
cycles   <- alloy$cycles
status <- alloy$status

# Probability estimation:
prob_tbl <- estimate_cdf(
  x = cycles,
  status = status,
  method = "johnson"
)

# Example 1: Probability Plot Weibull:
plot_weibull <- plot_prob(
  x = prob_tbl$x,
  y = prob_tbl$prob,
  status = prob_tbl$status,
  id = prob_tbl$id
)

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