Returns a compact summary of a hazard object, including model metadata,
fit diagnostics, and coefficient-level statistics when available.
Usage
# S3 method for class 'hazard'
summary(object, ...)See also
hazard() for model fitting, predict.hazard() for predictions.
vignette("fitting-hazard-models") for fitting workflows,
vignette("inference-diagnostics") for bootstrap CIs and diagnostics.
Examples
# -- Single-phase Weibull summary ------------------------------------
fit <- hazard(time = rexp(30, 0.5), status = rep(1L, 30),
theta = c(0.3, 1.0), dist = "weibull", fit = TRUE)
summary(fit)
#> hazard model summary
#> observations: 30
#> predictors: 0
#> dist: weibull
#> engine: native-r-m2
#> converged: TRUE
#> log-lik: -55.1318
#> evaluations: fn=31, gr=5
#>
#> Coefficients:
#> estimate std_error z_stat p_value
#> mu 0.4582700 0.1066703 4.296136 1.738007e-05
#> nu 0.8182644 0.1251257 6.539537 6.170969e-11
# \donttest{
# -- Multiphase model summary ----------------------------------------
set.seed(42)
n <- 200
dat <- data.frame(
time = rexp(n, rate = 0.25) + 0.01,
status = rbinom(n, size = 1, prob = 0.65)
)
fit_mp <- hazard(
survival::Surv(time, status) ~ 1,
data = dat,
dist = "multiphase",
phases = list(
early = hzr_phase("cdf", t_half = 0.5, nu = 2, m = 0,
fixed = "shapes"),
late = hzr_phase("cdf", t_half = 5, nu = 1, m = 0,
fixed = "shapes")
),
fit = TRUE,
control = list(n_starts = 5, maxit = 1000)
)
summary(fit_mp)
#> Multiphase hazard model (2 phases)
#> observations: 200
#> predictors: 0
#> dist: multiphase
#> phase 1: early - cdf (early risk)
#> phase 2: late - cdf (late risk)
#> engine: native-r-m2
#> converged: TRUE
#> log-lik: -428.716
#> evaluations: fn=26, gr=5
#>
#> Coefficients (internal scale):
#>
#> Phase: early (cdf)
#> estimate std_error z_stat p_value
#> log_mu -2.1153072 NA NA NA
#> log_t_half -0.6931472 NA NA NA
#> nu 2.0000000 NA NA NA
#> m 0.0000000 NA NA NA
#>
#> Phase: late (cdf)
#> estimate std_error z_stat p_value
#> log_mu 0.5511641 0.04098849 13.4468 3.214692e-41
#> log_t_half 1.6094379 NA NA NA
#> nu 1.0000000 NA NA NA
#> m 0.0000000 NA NA NA
# }