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Create a k-out-of-n system estimation model

Source: R/kofn.R
kofn.Rd

Constructs a likelihood model for component lifetime estimation from k-out-of-n system data. The system fails when k components have failed: k=1 is a series system (one failure kills it), k=m is a parallel system (all must fail).

Usage

kofn(
  k = 1L,
  m = 2L,
  component = dfr_exponential(),
  method = "mle",
  lifetime = "t",
  omega = "omega",
  lifetime_upper = "t_upper"
)

Arguments

k

System parameter: system fails when k components have failed. k=1 is series, k=m is parallel.

m

Number of components.

component

A dfr_dist prototype from flexhaz specifying the shared component distribution family. Currently supported: dfr_exponential and dfr_weibull. Parameter values on the prototype are ignored; they will be estimated from data.

method

Estimation method: "mle" (direct MLE) or "em" (EM algorithm, Weibull only).

lifetime

Column name for system lifetime (default "t").

omega

Column name for observation type (default "omega").

lifetime_upper

Column name for interval upper bound (default "t_upper").

Value

An S3 object of class c("exp_kofn"/"wei_kofn", "kofn", "likelihood_model").

Details

This model satisfies the likelihood_model concept from the likelihood.model package by providing methods for loglik, score, and hess_loglik.

The class hierarchy is:

  • "exp_kofn" or "wei_kofn" (distribution-specific dispatch)

  • "kofn" (shared methods)

  • "likelihood_model" (generic inference infrastructure)

The concrete subclass is determined by the type of component. The current closed-form machinery is homogeneous: all m components share the same distribution family, and component acts as a prototype for that family.

For non-k-of-n topologies (bridges, arbitrary coherent systems), use coherent_dist or one of the topology shortcuts in dist.structure directly. kofn is exclusively for the k-out-of-n family.

Examples

# Parallel system with 3 exponential components (k = m)
model <- kofn(k = 3, m = 3, component = dfr_exponential())
#> Error in dfr_exponential(): could not find function "dfr_exponential"
print(model)
#> Error: object 'model' not found

# Series system (k = 1)
model_series <- kofn(k = 1, m = 4, component = dfr_exponential())
#> Error in dfr_exponential(): could not find function "dfr_exponential"

# Weibull parallel system with EM estimation
model_wei <- kofn(k = 2, m = 2, component = dfr_weibull(), method = "em")
#> Error in dfr_weibull(): could not find function "dfr_weibull"