## ----lib, message = FALSE-----------------------------------------------------
library(polle)
library(data.table)

## ----simdata------------------------------------------------------------------
sim_single_stage_right_cens <- function(n = 2e3, zeta = c(0.7, 0.2), type = "right"){

  d <- sim_single_stage(n = n)
  pd <- policy_data(data = d,
                    action = "A",
                    covariates = c("Z", "L", "B"),
                    utility = "U")

  ld <- pd$stage_data

  ld[stage == 1, time := 1]
  ld[stage == 2, time := 2]

  ld[stage == 2, Z := d$Z]
  ld[stage == 2, L := d$L]
  ld[stage == 2, B := d$B]

  ## simulating the right censoring time
  ## only depending on the baseline covariate Z:
  C <- c(rexp(n, 1) / exp((-1) * cbind(1, as.numeric(d$Z)) %*% zeta))

  ld[stage == 1, time_c := C]
  ld[stage == 2, time_c := C]

  ld[, delta := time_c >= time]

  ld[delta == FALSE , event := 2]
  ld[delta == FALSE, A := NA]
  ld[delta == FALSE & stage == 2, U := NA]
  ld[delta == FALSE, U_A0 := 0]
  ld[delta == FALSE, U_A1 := 0]

  ld[ , tmp := shift(delta, fill = TRUE), by = list(id)]
  ld <- ld[tmp == TRUE, ]
  ld[ , time := pmin(time, time_c)]
  ld[ , time_c := NULL]
  ld[ , tmp := NULL]
  ld[ , delta := NULL]

  if (type == "interval"){
    ld[, time2 := time]
    ld[, time := shift(time, fill = 0), by = list(id)]
  }

  return(ld)
}

## ----simpd--------------------------------------------------------------------
set.seed(1)
ld <- sim_single_stage_right_cens(n = 5e2, type = "interval")
pd <- policy_data(data = ld, type = "long", action = "A", time = "time", time2 = "time2")

## ----pe-----------------------------------------------------------------------
pe <- policy_eval(
  policy_data = pd,
  policy = policy_def(1),
  m_model = q_glm(~ .),
  m_full_history = TRUE,
  c_models = list(c_cox(formula = ~ Z_1),
                  c_cox(formula = ~ Z_2*A_1)),
  c_full_history = TRUE
)
pe

## ----pl-----------------------------------------------------------------------
pe <- policy_eval(
  policy_data = pd,
  policy_learn = policy_learn(type = "blip", control = control_blip()),
  m_model = q_glm(~.),
  m_full_history = FALSE,
  c_models = c_cox(formula = ~ Z)
)
pe