mcf: Nonparametric Estimate for the Mean Cumulative Function

In jjw3952/mcotear: Contains helpful functions for use within MCOTEA

Nonparametric Estimate for the Mean Cumulative Function

Description

mcf calculates the sample nonparametric estimate of the mean cumulative function (expected number of failures at time t).

Usage

mcf(t, T, by = NULL)

Arguments

t	A list of failure time vectors. Each vector should indicate a different system, i.e. if you have multiple systems each systems' failure times should be in it's own vector.
T	A list of Total Time on Test (TTT) (i.e. test duration) vectors. The vectors in the list should be of length 1, and each vector should indicate a different system, i.e. if you have multiple systems each systems' TTT should be in it's own vector.
by	If providing a list of length > 1 this can be a vector that defines a name for each element of the list so as to return by system mcf estimates.

Value

The output will be a data.frame containing, the ordered failure times ("t") and corresponding nonparametric mcf estimate ("mcf").

See Also

power_law_process, power_law_mcf, trend_test, ttt, common_beta

Examples

data(amsaa)

mcf(
  t = split(amsaa$Time, amsaa$System),
  T = list(200,200,200),
  by = NULL)

mcf(
  t = split(amsaa$Time, amsaa$System),
  T = list(200,200,200),
  by = names(split(amsaa$Time, amsaa$System)))

mcf(
  t = split(amsaa$Time, amsaa$System),
  T = list(197.2,190.8,195.8),
  by = names(split(amsaa$Time, amsaa$System)))

mcf(
  t = split(amsaa$Time, amsaa$System),
  T = list(197.2,190.8,195.8),
  by = NULL)

rm(list = c("amsaa"))

jjw3952/mcotear documentation built on Sept. 2, 2023, 10:30 a.m.