weibulltools (version 0.5.4)

plot_prob_mix: Probability Plot for Separated Mixture Models

Description

This function is used to apply the graphical technique of probability plotting to univariate mixture models that where separated with the function mixmod_regression. A maximum of three subgroups can be 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.

Usage

plot_prob_mix(x, y, event, id = rep("XXXXXX", length(x)),
  distribution = c("weibull", "lognormal", "loglogistic"),
  reg_output = NULL, 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. Can be "weibull", "lognormal" or "loglogistic". Other distributions have not been implemented yet.

reg_output

a list provided by mixmod_regression which consists of values necessary to visualize the segments calculated by mixmod_regression.The default value of reg_output is NULL.

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".

Examples

# NOT RUN {
hours <- c(2, 28, 67, 119, 179, 236, 282, 317, 348, 387, 3, 31, 69, 135,
          191, 241, 284, 318, 348, 392, 5, 31, 76, 144, 203, 257, 286,
          320, 350, 412, 8, 52, 78, 157, 211, 261, 298, 327, 360, 446,
          13, 53, 104, 160, 221, 264, 303, 328, 369, 21, 64, 113, 168,
          226, 278, 314, 328, 377)
state <- c(1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,
          1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,
          1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
          0, 1, 1, 1, 1, 1, 1)
john <- johnson_method(x = hours, event = state)

mix_mod <- mixmod_regression(x = john$characteristic,
                             y = john$prob,
                             event = john$status,
                             distribution = "weibull")

plot_weibull_mix <- plot_prob_mix(x = john$characteristic,
                                  y = john$prob,
                                  event = john$status,
                                  id = john$id,
                                  distribution = "weibull",
                                  reg_output = mix_mod,
                                  title_main = "Mixture Weibull Analysis",
                                  title_x = "Time in Hours",
                                  title_y = "Probability of Failure",
                                  title_trace = "classification")
# }