Description Usage Arguments Details Value References Examples
This function is used to apply the graphical technique of probability plotting.
1 2 3 4 5 | 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")
|
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 |
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 |
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. |
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".
Returns a plotly object containing the layout of the probability plot
provided by plot_layout
and the plotting positions.
Meeker, William Q; Escobar, Luis A., Statistical methods for reliability data, New York: Wiley series in probability and statistics, 1998
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | # 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")
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