weibulltools (version 1.0.1)

plot_prob: Probability Plotting Method for Univariate Lifetime Distributions

Description

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

Usage

plot_prob(x, y, event, id = rep("XXXXXX", length(x)),
  distribution = c("weibull", "lognormal", "loglogistic", "normal",
  "logistic", "sev"), title_main = "Probability Plot",
  title_x = "Characteristic", title_y = "Unreliability",
  title_trace = "Sample")

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.

event

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

id

a character vector for the identification of every unit.

distribution

supposed distribution of the random variable. The value can be "weibull", "lognormal", "loglogistic", "normal", "logistic" or "sev" (smallest extreme value). Other distributions have not been implemented yet.

title_main

a character string which is assigned to the main title of the plot.

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 whis is assigned to the trace shown in the legend.

Value

Returns a plotly object containing the layout of the probability plot provided by plot_layout and the plotting positions.

Details

The marker label for x is determined by the first word provided in the argument title_x, i.e. if title_x = "Mileage in km" the x label of the marker is "Mileage".

The marker label for y is determined by the string provided in the argument title_y, i.e. if title_y = "Probability in percent" the y label of the marker is "Probability".

References

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

Examples

# NOT RUN {
# Alloy T7987 dataset taken from Meeker and Escobar(1998, p. 131)
cycles   <- c(300, 300, 300, 300, 300, 291, 274, 271, 269, 257, 256, 227, 226,
              224, 213, 211, 205, 203, 197, 196, 190, 189, 188, 187, 184, 180,
              180, 177, 176, 173, 172, 171, 170, 170, 169, 168, 168, 162, 159,
              159, 159, 159, 152, 152, 149, 149, 144, 143, 141, 141, 140, 139,
              139, 136, 135, 133, 131, 129, 123, 121, 121, 118, 117, 117, 114,
              112, 108, 104, 99, 99, 96, 94)
state <- c(rep(0, 5), rep(1, 67))

df_john <- johnson_method(x = cycles, event = state)

# Example 1: Probability Plot Weibull:
plot_weibull <- plot_prob(x = df_john$characteristic,
                          y = df_john$prob,
                          event = df_john$status,
                          id = df_john$id,
                          distribution = "weibull",
                          title_main = "Weibull Analysis",
                          title_x = "Cycles",
                          title_y = "Probability of Failure in %",
                          title_trace = "Failed Items")

# Example 2: Probability Plot Lognormal:
plot_lognormal <- plot_prob(x = df_john$characteristic,
                          y = df_john$prob,
                          event = df_john$status,
                          id = df_john$id,
                          distribution = "lognormal",
                          title_main = "Lognormal Analysis",
                          title_x = "Cycles",
                          title_y = "Probability of Failure in %",
                          title_trace = "Failed Items")
# }