weibulltools (version 1.0.1)

mr_method: Estimation of Failure Probabilities using Median Ranks

Description

This non-parametric approach (Median Ranks) is used to estimate the failure probabilities in terms of complete data. Two methods are available to estimate the cumulative distribution function F(t):

• "benard"; Benard's approximation for Median Ranks

• "invbeta"; Exact Median Ranks using the inverse beta distribution

Usage

mr_method(x, event = rep(1, length(x)), id = rep("XXXXXX", length(x)),
  method = "benard")

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.

event

a vector of ones indicating that every unit i has failed.

id

a character vector for the identification of every unit.

method

method for the estimation of the cdf. Can be "benard" (default) or "invbeta".

Value

A data frame containing id, lifetime characteristic, status of the unit, the rank and the estimated failure probability.

Examples

# NOT RUN {
# Example 1
obs   <- seq(10000, 100000, 10000)
state <- rep(1, length(obs))
uic   <- c("3435", "1203", "958X", "XX71", "abcd", "tz46",
           "fl29", "AX23", "Uy12", "kl1a")

df_mr <- mr_method(x = obs, event = state, id = uic,
                   method = "benard")

# Example 2
df_mr_invbeta <- mr_method(x = obs, event = state, id = uic,
                           method = "invbeta")
# }