CoxRun: Fully runs a cox or fine-gray regression model, returning the...

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

Fully runs a cox or fine-gray regression model, returning the model and results

Description

CoxRun uses a formula, data.table, and list of controls to prepare and run a Colossus cox or fine-gray regression function

Usage

CoxRun(
  model,
  df,
  a_n = list(c(0)),
  keep_constant = c(0),
  control = list(),
  gradient_control = list(),
  single = FALSE,
  observed_info = FALSE,
  cons_mat = as.matrix(c(0)),
  cons_vec = c(0),
  norm = "null",
  ...
)

Arguments

model	either a formula written for the get_form function, or the model result from the get_form function.
df	a data.table containing the columns of interest
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.
keep_constant	binary values to denote which parameters to change
control	list of parameters controlling the convergence, see the Control_Options vignette for details
gradient_control	a list of control options for the gradient descent algorithm. If any value is given, a gradient descent algorithm is used instead of Newton-Raphson. See the Control_Options vignette for details
single	a boolean to denote that only the log-likelihood should be calculated and returned, no derivatives or iterations
observed_info	a boolean to denote that the observed information matrix should be used to calculate the standard error for parameters, not the expected information matrix
cons_mat	Matrix containing coefficients for a system of linear constraints, formatted as matrix
cons_vec	Vector containing constants for a system of linear constraints, formatted as vector
norm	methods used to normalize the covariates. Default is 'null' for no normalization. Other options include 'max' to normalize by the absolute maximum and 'mean' to normalize by the mean
...	can include the named entries for the control list parameter

Value

returns a class fully describing the model and the regression results

See Also

Other Cox Wrapper Functions: CoxRunMulti(), LikelihoodBound.coxres()

Examples

library(data.table)
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)
)
control <- list(
  "ncores" = 1, "lr" = 0.75, "maxiters" = c(1, 1),
  "halfmax" = 1
)
formula <- Cox(Starting_Age, Ending_Age, Cancer_Status) ~
  loglinear(a, b, c, 0) + plinear(d, 0) + multiplicative()
res <- CoxRun(formula, df,
  a_n = list(c(1.1, -0.1, 0.2, 0.5), c(1.6, -0.12, 0.3, 0.4)),
  control = control
)

Colossus documentation built on Feb. 20, 2026, 5:10 p.m.