## ----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)
library(parallel)

## -----------------------------------------------------------------------------
names <- c("a", "b", "c", "d")
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"

a_n <- c(0.1, 0.1, 0.1, 0.1)

model <- outcome ~ loglinear(a, 0) + linear(b, c, 1) +
  plinear(d, 2) + ME()

## -----------------------------------------------------------------------------
df <- data.table(
  UserID = c(112, 114, 213, 214, 115, 116, 117),
  Starting_Age = c(18, 20, 18, 19, 21, 20, 18),
  Ending_Age = c(30, 45, 57, 47, 36, 60, 55),
  Cancer_Status = c(0, 0, 1, 0, 1, 0, 0),
  a = c(0, 1, 1, 0, 1, 0, 1),
  b = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
  c = c(10, 11, 10, 11, 12, 9, 11),
  d = c(0, 0, 0, 1, 1, 1, 1)
)
# For the interval case
tstart <- "Starting_Age"
tend <- "Ending_Age"
event <- "Cancer_Status"

## -----------------------------------------------------------------------------
df$Person_Years <- df$Ending_Age - df$Starting_Age
pyr <- "Person_Years"
event <- "Cancer_Status"

## -----------------------------------------------------------------------------
# For the interval case
RHS <- Cox(Starting_Age, Ending_Age, Cancer_Status) ~ risk_factors

# Supposing we had left truncated data the following would change
RHS <- Cox(tstart = Starting_Age, event = Cancer_Status) ~ risk_factors

# and with right truncated data the following is used
RHS <- Cox(Ending_Age, Cancer_Status) ~ risk_factors

## -----------------------------------------------------------------------------
keep_constant <- c(0, 0, 0, 0)

control <- list(
  ncores = 1, lr = 0.75, maxiter = 100, halfmax = 5, epsilon = 1e-3,
  dbeta_max = 0.5, ll_epsilon = 1e-4, step_max = 1.0,
  verbose = 2
)

## -----------------------------------------------------------------------------
# assuming the table of covariates is stored in a data.table "df"

model_cox <- Cox(Starting_Age, Ending_Age, Cancer_Status) ~ loglinear(a, 0) +
  linear(b, c, 1) + plinear(d, 2) + multiplicative()

e <- CoxRun(model_cox, df, a_n = a_n, control = control)
print(e)

# or a Poisson model regression
model_pois <- Poisson(Person_Years, Cancer_Status)  ~ loglinear(a, 0) + linear(b, c, 1) + plinear(d, 2) + multiplicative()
e <- PoisRun(model_pois, df, a_n = a_n, control = control)
print(e)