forward_selection: Forward Selection Procedure for Parametric Illness-Death...
In harrisonreeder/SemiCompRisksPen: Penalized Models for Semi-Competing Risks

forward_selection

R Documentation

Forward Selection Procedure for Parametric Illness-Death Model

Description

This method is meant as a comparator for the penalized estimation procedure.

Usage

forward_selection(
  vars,
  data,
  na.action = "na.fail",
  subset = NULL,
  hazard = c("weibull"),
  frailty = TRUE,
  model,
  knots_list = NULL,
  fixed1 = character(0),
  fixed2 = character(0),
  fixed3 = character(0),
  optim_method = "L-BFGS",
  select_crit = "bic",
  verbose = 0,
  control = NULL
)

Arguments

`vars`	character string with all of the possible variable names to be searched through.
`data`	a `data.frame` in which to interpret the variables named in Formula.
`na.action`	how NAs are treated. See `model.frame`.
`subset`	a specification of the rows to be used: defaults to all rows. See `model.frame`.
`hazard`	String specifying the form of the baseline hazard.
`frailty`	Boolean indicating whether a gamma distributed subject-specific frailty should be included. Currently this must be set to TRUE.
`model`	String specifying the transition assumption
`knots_list`	Used for hazard specifications besides Weibull, a list of three increasing sequences of integers, each corresponding to the knots for the flexible model on the corresponding transition baseline hazard. If `NULL`, will be created by `get_default_knots_list`.
`fixed1`	a string vector for parameters you want guaranteed included in arm 1
`fixed2`	a string vector for parameters you want guaranteed included in arm 2
`fixed3`	a string vector for parameters you want guaranteed included in arm 3
`optim_method`	a string naming which `optim` method should be used.
`select_crit`	a string indicating what criterion should be used to determine whether a covariate should be added.
`verbose`	Numeric indicating the amount of iteration information should be printed to the user. Higher numbers provide more detailed information to user, but will slow down the algorithm.
`control`	a list of control attributes passed directly into the `optim` function.