FreqID_HReg_Rpath: Fit Penalized Parametric Frailty Illness-Death Model Solution...

In hreed7/SemiCompRisksPen: Penalized Models for Semi-Competing Risks

Fit Penalized Parametric Frailty Illness-Death Model Solution Path

Description

Fit Penalized Parametric Frailty Illness-Death Model Solution Path

Usage

FreqID_HReg_Rpath(
  Formula,
  data,
  na.action = "na.fail",
  subset = NULL,
  hazard = c("weibull"),
  frailty = TRUE,
  model,
  knots_list = NULL,
  penalty = c("scad", "mcp", "lasso"),
  lambda_path = NULL,
  lambda_target = 0,
  N_path_steps = 40,
  a = NULL,
  mm_epsilon = 1e-08,
  select_tol = 1e-04,
  fusion_tol = 0.001,
  penalty_fusedcoef = c("none", "fusedlasso"),
  lambda_fusedcoef_path = 0,
  penalty_fusedbaseline = c("none", "fusedlasso"),
  lambda_fusedbaseline = 0,
  penweights_list = list(),
  mu_smooth_path = 0,
  fit_method = "prox_grad",
  startVals = NULL,
  ball_L2 = Inf,
  warm_start = TRUE,
  step_size_min = 1e-06,
  step_size_max = 1e+06,
  step_size_init = 1,
  step_size_scale = 0.5,
  step_delta = 0.5,
  maxit = 300,
  extra_starts = 0,
  conv_crit = "nll_pen_change",
  conv_tol = 1e-06,
  standardize = TRUE,
  verbose = 0
)

Arguments

Formula	a Formula object, with the outcome on the left of a ~, and covariates on the right. It is of the form, time to non-terminal event + corresponding censoring indicator | time to terminal event + corresponding censoring indicator ~ covariates for h_1 | covariates for h_2 | covariates for h_3. For example, y_1 + delta_1 | y_2 + delta_2 ~ x_1 | x_2 | x_3.
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.
penalty	A string value indicating the form of parameterwise penalty to apply. "lasso", "scad", and "mcp" are the options.
a	For two-parameter penalty functions (e.g., scad and mcp), the second parameter.
mm_epsilon	Positive numeric tolerance parameter for smooth approximation of absolute value function at 0.
select_tol	Positive numeric value for thresholding estimates to be equal to zero.
fusion_tol	Positive numeric value for thresholding estimates that are close to being considered fused, for the purposes of estimating degrees of freedom.
penalty_fusedcoef	A string value indicating the form of the fusion penalty to apply to the regression parameters. "none" and "fusedlasso" are the options.
penalty_fusedbaseline	A string value indicating the form of the fusion penalty to apply to the baseline hazard parameters. "none" and "fusedlasso" are the options.
lambda_fusedbaseline	The strength of the fusion penalty on the regression parameters. Either a single non-negative numeric value for all three transitions, or a length 3 vector with elements corresponding to the three transitions.
penweights_list	A list of numeric vectors representing weights for each penalty term (e.g., for adaptive lasso.) Elements of the list should be indexed by the names "coef1", "coef2", "coef3", "fusedcoef12", "fusedcoef13", "fusedcoef23", "fusedbaseline12", "fusedbaseline13", and "fusedbaseline23"
startVals	A numeric vector of parameter starting values, arranged as follows: the first k_1+k_2+k_3 elements correspond to the baseline hazard parameters, then the k_1+k_2+k_3+1 element corresponds to the gamma frailty log-variance parameter, then the lastq_1+q_2+q_3 elements correspond with the regression parameters. If set to NULL, will be generated automatically using get_start.
ball_L2	Positive numeric value for l_2 ball constraint around the origin for the regression parameters. Typically set to Inf indicating no constraint, otherwise equivalent to an extra l_2 penalty.
step_size_min	Positive numeric value for the minimum allowable step size to allow during backtracking.
step_size_max	Positive numeric value for the maximum allowable step size to allow by size increase at each iteration.
step_size_init	Positive numeric value for the initial step size.
step_size_scale	Positive numeric value for the multiplicative change in step size at each step of backtracking.
maxit	Positive integer maximum number of iterations.
conv_crit	String (possibly vector) giving the convergence criterion.
conv_tol	Positive numeric value giving the convergence tolerance for the chosen criterion.
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.
optimization_method	vector of optimization methods to apply. Method achieving lowest objective function will be final reported result.

Value

A list.

hreed7/SemiCompRisksPen documentation built on Dec. 15, 2024, 5:41 p.m.