weibulltools (version 1.0.1)

mixmod_regression: Mixture Model Identification using Segmented Regression

Description

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.

Usage

mixmod_regression(x, y, event, distribution = c("weibull", "lognormal",
  "loglogistic"), conf_level = 0.95)

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).

distribution

supposed distribution of the random variable. The value can be "weibull", "lognormal" or "loglogistic". Other distributions have not been implemented yet.

conf_level

confidence level of the interval. The default value is conf_level = 0.95.

Value

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.

References

Doganaksoy, N.; Hahn, G.; Meeker, W. Q., Reliability Analysis by Failure Mode, Quality Progress, 35(6), 47-52, 2002

Examples

# NOT RUN {
# 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")

# }