## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
set.seed(42)

## ----setup--------------------------------------------------------------------
library(kofn)
library(flexhaz)

## -----------------------------------------------------------------------------
# A 2-of-3 exponential system: system fails when 2 of 3 components fail.
model <- kofn(k = 2, m = 3, component = dfr_exponential())

# Generate 200 complete (Scheme 0) observations.
gen <- rdata(model)
df <- gen(theta = c(1, 2, 3), n = 100)

# Fit via maximum likelihood.
fitter <- fit(model)
result <- fitter(df, n_starts = 1)
result$converged
coef(result)

## -----------------------------------------------------------------------------
sum(coef(result))
sum(c(1, 2, 3))

## -----------------------------------------------------------------------------
# Right-censored: systems that fail after tau = 2 get recorded at t = 2
df_cens <- gen(c(1, 2, 3), n = 100,
               observe = observe_right_censor(tau = 2))
table(df_cens$omega)

## -----------------------------------------------------------------------------
ll <- loglik(model)
ll(df_cens, c(1, 2, 3))  # finite; right-censored obs contribute log S(t)

## -----------------------------------------------------------------------------
model_wei <- kofn(k = 2, m = 2, component = dfr_weibull(), method = "em")
gen_wei <- rdata(model_wei)
df_wei <- gen_wei(theta = c(1.5, 2.0, 2.0, 3.0), n = 100)
result_wei <- fit(model_wei)(df_wei, n_starts = 1)
result_wei$shapes
result_wei$scales

## -----------------------------------------------------------------------------
s1gen <- rdata_scheme1(model)
df_s1 <- s1gen(theta = c(1, 2, 3), n = 200, delta = 0.5)
ll_s1 <- loglik_scheme1(model)
ll_s1(df_s1, c(1, 2, 3))

## -----------------------------------------------------------------------------
rgen <- rdata_masked(model)
df_masked <- rgen(theta = c(1, 2, 3), n = 100, p_mask = 0.3)
ll_masked <- loglik_masked(model)
ll_masked(df_masked, c(1, 2, 3))

## ----eval = FALSE-------------------------------------------------------------
# res <- compare_fisher_info(
#   rates = c(0.5, 0.3), n = 50, delta = 1.0, n_rep = 10,
#   component = dfr_exponential()
# )
# res$median_det