Fit Weibull k-out-of-n system model

Returns a closure that fits the model to data. Two methods are available:

Usage

# S3 method for class 'wei_kofn'
fit(object, ...)

Arguments

object

A wei_kofn object.

...

Additional arguments (currently unused).

Value

A function function(df, par0, ...) that returns a fisher_mle object (from the likelihood.model package).

Details

"em"

EM algorithm for parallel systems (k = m only). Treats the identity of the last-failing component as latent data. Uses truncated Weibull moments for the E-step and profile optimization over shape with closed-form scale for the M-step.

"mle"

Direct MLE via L-BFGS-B with Nelder-Mead fallback. Works for any k-out-of-n structure.

The returned solver accepts these additional arguments:

par0

Initial parameter vector (length 2*m, interleaved shape/scale). If NULL, method-of-moments initialization is used.

n_starts

Number of multi-start attempts (default: 5).

tol

Convergence tolerance for EM (default: 1e-8).

maxiter

Maximum EM iterations (default: 2000).

shape_bounds

Bounds for shape optimization in EM (default: c(0.05, 15)).

verbose

Print iteration progress (default: FALSE).

Examples

# \donttest{
model <- kofn(k = 2, m = 2, component = dfr_weibull())
#> Error in dfr_weibull(): could not find function "dfr_weibull"
set.seed(42)
df <- rdata(model)(c(1.5, 2.0, 2.0, 3.0), n = 50)
#> Error: object 'model' not found
result <- fit(model)(df)
#> Error: object 'model' not found
coef(result)
#> Error: object 'result' not found
# }