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Returns a closure that generates k-out-of-n system data under periodic inspection. Each observation yields the exact system failure time and interval-censored component failure times.

Usage

rdata_scheme1(model, ...)

Arguments

model

A kofn model object (exponential or Weibull).

...

Additional arguments (currently unused).

Value

A function function(theta, n, delta) returning a data frame with columns:

t

System failure time (exact).

comp_lower_j

Lower bound of inspection interval for component j.

comp_upper_j

Upper bound of inspection interval for component j.

The data frame has attributes comp_times (true component times), delta (inspection interval), and par (true parameters).

Details

Note: The Scheme 1 likelihood uses a composite approximation that treats the system density and component interval contributions as independent. This works well for k >= 2 but is unreliable for series systems (k = 1), where surviving components' intervals should be conditioned on survival past T_sys. For series systems, use the maskedcauses package instead.

Examples

model <- kofn(k = 2, m = 2, component = dfr_exponential())
#> Error in dfr_exponential(): could not find function "dfr_exponential"
gen <- rdata_scheme1(model)
#> Error: object 'model' not found
set.seed(1)
df <- gen(theta = c(1, 2), n = 20, delta = 1.0)
#> Error in gen(theta = c(1, 2), n = 20, delta = 1): could not find function "gen"
head(df)
#>                                               
#> 1 function (x, df1, df2, ncp, log = FALSE)    
#> 2 {                                           
#> 3     if (missing(ncp))                       
#> 4         .Call(C_df, x, df1, df2, log)       
#> 5     else .Call(C_dnf, x, df1, df2, ncp, log)
#> 6 }