R/plot_functions.R
plot_prob.Rd
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.
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"),
...
)
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.
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.
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".
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)