Description Usage Arguments Value References Examples
This method uses piecewise linear regression to separate the data in subgroups, if appropriate. Since this happens in an automated fashion the function tends to overestimate the number of breakpoints and therefore returns too many subgroups. This problem is already stated in the documentation of the function segmented.lm, which is part of the segmented package. A maximum of three subgroups can be obtained.
1 2  mixmod_regression(x, y, event, distribution = c("weibull", "lognormal",
"loglogistic"), conf_level = 0.95)

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). 
distribution 
supposed distribution of the random variable. The
value can be 
conf_level 
confidence level of the interval. The default value is

Returns a list where the length of the list depends on the number of identified subgroups. Each list has the same information as provided by rank_regression. Additionally each list has an element that specifies the range regarding the lifetime data for every subgroup.
Doganaksoy, N.; Hahn, G.; Meeker, W. Q., Reliability Analysis by Failure Mode, Quality Progress, 35(6), 4752, 2002
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  # Data is taken from given reference:
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")

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