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

## ----setup--------------------------------------------------------------------
library(Colossus)
library(data.table)

## ----eval=FALSE---------------------------------------------------------------
#  Strat_Col <- "e"
#  e <- RunCoxRegression_STRATA(
#    df, time1, time2, event, names, term_n, tform, keep_constant,
#    a_n, modelform, fir, der_iden, control, Strat_Col
#  )

## ----eval=FALSE---------------------------------------------------------------
#  Strat_Col <- c("e")
#  e <- RunPoissonRegression_STRATA(
#    df, pyr, event, names, term_n, tform, keep_constant,
#    a_n, modelform, fir, der_iden, control, Strat_Col
#  )

## ----eval=FALSE---------------------------------------------------------------
#  e <- RunCoxRegression_Basic(
#    df, time1, time2, event, names,
#    keep_constant, a_n, der_iden, control
#  )

## ----eval=FALSE---------------------------------------------------------------
#  e <- RunCoxRegression_Single(
#    df, time1, time2, event, names, term_n, tform,
#    a_n, modelform, fir, control
#  )
#  
#  e <- RunPoissonRegression_Single(
#    df, pyr, event, names, term_n, tform,
#    a_n, modelform, fir, control
#  )

## ----eval=FALSE---------------------------------------------------------------
#  df$censor <- (df$lung == 0) # censoring column made
#  event <- "censor" # event type switched to censoring
#  
#  plot_options <- list("name" = "run_2", "verbose" = FALSE, "studyID" = "studyID", "age_unit" = "years")
#  # modified plotting function used to get censoring weights
#  dft <- GetCensWeight(
#    df, time1, time2, event, names, term_n, tform, keep_constant,
#    a_n, modelform, fir, control, plot_options
#  ) # generates a survival curve
#  t_ref <- dft$t
#  surv_ref <- dft$surv
#  t_c <- df$t1
#  cens_weight <- approx(t_ref, surv_ref, t_c, rule = 2)$y
#  # the surviving proportions used as censoring weight
#  event <- "lung" # event switched back
#  
#  e <- RunCoxRegression_CR(
#    df, time1, time2, event, names, term_n, tform, keep_constant,
#    a_n, modelform, fir, der_iden, control, cens_weight
#  )

## ----eval=TRUE----------------------------------------------------------------
a <- c(0, 0, 0, 1, 1, 1)
b <- c(1, 1, 1, 2, 2, 2)
c <- c(0, 1, 2, 2, 1, 0)
d <- c(1, 1, 0, 0, 1, 1)
e <- c(0, 1, 1, 1, 0, 0)
df <- data.table("t0" = a, "t1" = b, "e0" = c, "e1" = d, "fac" = e)
time1 <- "t0"
time2 <- "t1"
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
events <- c("e0", "e1")

## ----eval=TRUE----------------------------------------------------------------
names_e0 <- c("fac")
names_e1 <- c("fac")
names_shared <- c("t0", "t0")
term_n_e0 <- c(0)
term_n_e1 <- c(0)
term_n_shared <- c(0, 0)
tform_e0 <- c("loglin")
tform_e1 <- c("loglin")
tform_shared <- c("quad_slope", "loglin_top")
keep_constant_e0 <- c(0)
keep_constant_e1 <- c(0)
keep_constant_shared <- c(0, 0)
a_n_e0 <- c(-0.1)
a_n_e1 <- c(0.1)
a_n_shared <- c(0.001, -0.02)
name_list <- list("shared" = names_shared, "e0" = names_e0, "e1" = names_e1)
term_n_list <- list("shared" = term_n_shared, "e0" = term_n_e0, "e1" = term_n_e1)
tform_list <- list("shared" = tform_shared, "e0" = tform_e0, "e1" = tform_e1)
keep_constant_list <- list(
  "shared" = keep_constant_shared,
  "e0" = keep_constant_e0, "e1" = keep_constant_e1
)
a_n_list <- list("shared" = a_n_shared, "e0" = a_n_e0, "e1" = a_n_e1)

## ----eval=TRUE----------------------------------------------------------------
Joint_Multiple_Events(
  df, events, name_list, term_n_list,
  tform_list, keep_constant_list, a_n_list
)

## ----eval=TRUE----------------------------------------------------------------
der_iden <- 0
modelform <- "M"
fir <- 0
control <- list(
  "ncores" = 1, "lr" = 0.75, "maxiter" = 10, "halfmax" = 5, "epsilon" = 1e-3,
  "dbeta_max" = 0.5, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE,
  "dose_abs_max" = 100.0, "verbose" = FALSE, "ties" = "breslow", "double_step" = 1
)
guesses_control <- list(
  "maxiter" = 10, "guesses" = 10, "lin_min" = 0.001, "lin_max" = 1,
  "loglin_min" = -1, "loglin_max" = 1, "lin_method" = "uniform", "loglin_method" = "uniform",
  strata = FALSE
)
Strat_Col <- "f"
RunPoissonRegression_Joint_Omnibus(
  df, pyr, events, name_list, term_n_list,
  tform_list, keep_constant_list, a_n_list,
  modelform, fir, der_iden, control, Strat_Col
)

## ----eval=FALSE---------------------------------------------------------------
#  a_n <- list(c(1, 1, 1), c(1, 2, 1), c(1, 2, 2), c(2, 1, 1))
#  
#  control$maxiter <- 5 # runs each (4) starts 1 iteration, and then runs the best 5 iterations
#  control$maxiters <- c(1, 1, 1, 1, 5) # runs each (4) starts 1 iteration, and then runs the best 5 iterations
#  control$maxiters <- c(5, 5, 5, 5, 5) # runs each (4) starts 5 iterations, and then runs the best 5 iterations
#  
#  e <- RunCoxRegression_Omnibus(df, time1, time2, event,
#    names, term_n, tform, keep_constant,
#    a_n, modelform,
#    control = control
#  )