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

## ----setup--------------------------------------------------------------------
library(survobj)
library(survival)

## ----fig.height=4, fig.width=7, fig.align='center'----------------------------
s_obj <- s_exponential(fail = 0.4, t = 2)
ggplot_survival_random(s_obj, timeto =2, subjects = 1000, nsim= 10, alpha = 0.3)

## ----fig.height=4, fig.width=7, fig.align='center'----------------------------
s_obj <- s_exponential(fail = 0.4, t = 2)
group <- c(rep(0,500), rep(1,500))
hr_vector <- c(rep(1,500),rep(2,500))
times <- rsurvhr(s_obj, hr_vector)
plot(survfit(Surv(times)~group), xlim=c(0,5))

## ----fig.height=4, fig.width=7, fig.align='center'----------------------------
s_obj <- s_exponential(fail = 0.4, t = 2)
ggplot_survival_hr(s_obj, hr = 2, nsim = 10, subjects = 1000, timeto = 5)

## ----fig.height=4, fig.width=7, fig.align='center'----------------------------
s_obj <- s_lognormal(scale = 2, shape = 0.5)
ggplot_survival_aft(s_obj, aft = 2, nsim = 10, subjects = 1000, timeto = 5)

## ----fig.height=4, fig.width=7, fig.align='center'----------------------------
s_obj <- s_lognormal(scale = 2, shape = 0.5)
ggplot_survival_eh(s_obj, aft = 2, hr = 0.5, nsim = 10, subjects = 1000, timeto = 5)

## -----------------------------------------------------------------------------
# Weibull baseline with an increasing hazard (shape > 1)
s_obj <- s_weibull(scale = 1, shape = 2)
n <- 5000
hr <- rep(1, n)

# First episode, common to both processes
t1 <- rsurvhr(s_obj, hr)

# Renewal process: risk resets at each episode
t2 <- renewhr(s_obj, hr, t1)
t3 <- renewhr(s_obj, hr, t2)
c(gap1 = mean(t1), gap2 = mean(t2 - t1), gap3 = mean(t3 - t2))

# Non-homogeneous Poisson process: risk keeps accumulating, never resets
p2 <- nhpphr(s_obj, hr, t1)
p3 <- nhpphr(s_obj, hr, p2)
c(gap1 = mean(t1), gap2 = mean(p2 - t1), gap3 = mean(p3 - p2))