RunCoxRegression_Guesses_CPP: Performs basic Cox Proportional Hazards regression, Generates...

In Colossus: "Risk Model Regression and Analysis with Complex Non-Linear Models"

Performs basic Cox Proportional Hazards regression, Generates multiple starting guesses on c++ side

Description

RunCoxRegression_Guesses_CPP uses user provided data, time/event columns, vectors specifying the model, and options to control the convergence and starting positions. Has additional options to starting with several initial guesses

Usage

RunCoxRegression_Guesses_CPP(
  df,
  time1 = "%trunc%",
  time2 = "%trunc%",
  event0 = "event",
  names = c("CONST"),
  term_n = c(0),
  tform = "loglin",
  keep_constant = c(0),
  a_n = c(0),
  modelform = "M",
  control = list(),
  guesses_control = list(),
  strat_col = "null",
  model_control = list(),
  cens_weight = "null"
)

Arguments

df	a data.table containing the columns of interest
time1	column used for time period starts
time2	column used for time period end
event0	column used for event status
names	columns for elements of the model, used to identify data columns
term_n	term numbers for each element of the model
tform	list of string function identifiers, used for linear/step
keep_constant	binary values to denote which parameters to change
a_n	list of initial parameter values, used to determine the number of parameters. May be either a list of vectors or a single vector.
modelform	string specifying the model type: M, ME, A, PA, PAE, GMIX, GMIX-R, GMIX-E
control	list of parameters controlling the convergence, see Def_Control() for options or vignette("Control_Options")
guesses_control	list of parameters to control how the guessing works, see Def_Control_Guess() for options or vignette("Control_Options")
strat_col	column to stratify by if needed
model_control	controls which alternative model options are used, see Def_model_control() for options and vignette("Control_Options") for further details
cens_weight	column containing the row weights

Value

returns a list of the final results

See Also

Other Cox Wrapper Functions: CoxCurveSolver(), RunCaseControlRegression_Omnibus(), RunCoxNull(), RunCoxRegression(), RunCoxRegression_Basic(), RunCoxRegression_CR(), RunCoxRegression_Omnibus(), RunCoxRegression_Omnibus_Multidose(), RunCoxRegression_Single(), RunCoxRegression_Strata(), RunCoxRegression_Tier_Guesses()

Examples

library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::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),
  "e" = c(0, 0, 1, 0, 0, 0, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
a_n <- c(1.1, -0.1, 0.2, 0.5) # used to test at a specific point
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
keep_constant <- c(0, 0, 0, 0)
control <- list(
  "ncores" = 2, "lr" = 0.75, "maxiter" = 5,
  "halfmax" = 5, "epsilon" = 1e-3,
  "deriv_epsilon" = 1e-3, "abs_max" = 1.0,
  "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 <- "e"
options(warn = -1)
e <- RunCoxRegression_Guesses_CPP(
  df, time1, time2, event, names, term_n,
  tform, keep_constant, a_n, modelform,
  control, guesses_control, strat_col
)

Colossus documentation built on June 8, 2025, 1:10 p.m.