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

## ----setup--------------------------------------------------------------------
Sys.setenv("OMP_THREAD_LIMIT" = 1) # Reducing core use, to avoid accidental use of too many cores
library(Colossus)
library(data.table)
if (system.file(package = "survival") != "") {
  library(survival)
}
library(dplyr)

## ----eval=TRUE----------------------------------------------------------------
if (system.file(package = "survival") != "") {
  data(cancer, package = "survival")
  cancer %>% setDT()
  df <- copy(cancer)
} else {
  status <- c(2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1)
  sex <- c(1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 2)
  time <- c(306, 455, 1010, 210, 883, 1022, 310, 361, 218, 166, 170, 654, 728, 71, 567, 144, 613, 707, 61, 88, 301, 81, 624, 371, 394, 520, 574, 118, 390, 12, 473, 26, 533, 107, 53, 122, 814, 965, 93, 731, 460, 153, 433, 145, 583, 95, 303, 519, 643, 765, 735, 189, 53, 246, 689, 65, 5, 132, 687, 345, 444, 223, 175, 60, 163, 65, 208, 821, 428, 230, 840, 305, 11, 132, 226, 426, 705, 363, 11, 176, 791, 95, 196, 167, 806, 284, 641, 147, 740, 163, 655, 239, 88, 245, 588, 30, 179, 310, 477, 166, 559, 450, 364, 107, 177, 156, 529, 11, 429, 351, 15, 181, 283, 201, 524, 13, 212, 524, 288, 363, 442, 199, 550, 54, 558, 207, 92, 60, 551, 543, 293, 202, 353, 511, 267, 511, 371, 387, 457, 337, 201, 404, 222, 62, 458, 356, 353, 163, 31, 340, 229, 444, 315, 182, 156, 329, 364, 291, 179, 376, 384, 268, 292, 142, 413, 266, 194, 320, 181, 285, 301, 348, 197, 382, 303, 296, 180, 186, 145, 269, 300, 284, 350, 272, 292, 332, 285, 259, 110, 286, 270, 81, 131, 225, 269, 225, 243, 279, 276, 135, 79, 59, 240, 202, 235, 105, 224, 239, 237, 173, 252, 221, 185, 92, 13, 222, 192, 183, 211, 175, 197, 203, 116, 188, 191, 105, 174, 177)
  age <- c(74, 68, 56, 57, 60, 74, 68, 71, 53, 61, 57, 68, 68, 60, 57, 67, 70, 63, 56, 57, 67, 49, 50, 58, 72, 70, 60, 70, 53, 74, 69, 73, 48, 60, 61, 62, 65, 66, 74, 64, 70, 73, 59, 60, 68, 76, 74, 63, 74, 50, 72, 63, 68, 58, 59, 62, 65, 57, 58, 64, 75, 48, 73, 65, 69, 68, 67, 64, 68, 67, 63, 48, 74, 40, 53, 71, 51, 56, 81, 73, 59, 55, 42, 44, 44, 71, 62, 61, 44, 72, 63, 70, 66, 57, 69, 72, 69, 71, 64, 70, 58, 69, 56, 63, 59, 66, 54, 67, 55, 75, 69, 44, 80, 75, 54, 76, 49, 68, 66, 80, 75, 60, 69, 72, 70, 66, 50, 64, 77, 48, 59, 53, 47, 55, 67, 74, 58, 56, 54, 56, 73, 74, 76, 65, 57, 53, 71, 54, 82, 59, 70, 60, 62, 53, 55, 69, 68, 62, 63, 56, 62, 44, 69, 63, 64, 57, 60, 46, 61, 65, 61, 58, 56, 43, 53, 59, 56, 55, 53, 74, 60, 39, 66, 65, 51, 45, 72, 58, 64, 53, 72, 52, 50, 64, 71, 70, 63, 64, 52, 60, 64, 73, 63, 50, 63, 62, 55, 50, 69, 59, 60, 67, 69, 64, 65, 65, 41, 76, 70, 57, 67, 71, 76, 77, 39, 75, 66, 58)

  df <- data.table(
    "status" = status,
    "sex" = sex,
    "time" = time,
    "age" = age
  )
}

cancer$status <- as.integer(cancer$status == 2)
cancer$time <- cancer$time / 100

cancer$erate <- 0.5

## ----eval=TRUE----------------------------------------------------------------
a_n <- c(1)
control <- list(
  "ncores" = 1, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-9,
  "deriv_epsilon" = 1e-9, "verbose" = 2
)
e <- PoisRun(Poisson(time, status) ~ linear(erate), cancer,
  a_n = a_n, control = control
)
print(e)

## ----eval=TRUE----------------------------------------------------------------
a_n <- c(1, 1)

e <- PoisRun(Poisson(time, status) ~ linear(erate, 0) + linear(sex, 1),
  cancer,
  a_n = a_n, control = control
)
print(e)