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
plot_prob(x, ...) # S3 method for 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 wt_model plot_prob( x, title_main = "Probability Plot", title_x = "Characteristic", title_y = "Unreliability", title_trace = "Sample", plot_method = c("plotly", "ggplot2"), ... )
Further arguments passed to or from other methods. Currently not used.
Supposed distribution of the random variable.
A character string which is assigned to the main title.
A character string which is assigned to the title of the x axis.
A character string which is assigned to the title of the y axis.
A character string which is assigned to the legend trace.
Package, which is used for generating the plot output.
A plot object containing the probability plot.
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.
plot_method == "plotly" the marker label for x and y are determined by
the first word provided in the argument
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
Meeker, William Q; Escobar, Luis A., Statistical methods for reliability data, New York: Wiley series in probability and statistics, 1998
# 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)