FreqID_HReg_R: Fit Parametric Frailty Illness-Death Model for Semi-Competing...
In hreed7/SemiCompRisksPen: Penalized Models for Semi-Competing Risks

FreqID_HReg_R

R Documentation

Fit Parametric Frailty Illness-Death Model for Semi-Competing Risks Data

Description

Fit Parametric Frailty Illness-Death Model for Semi-Competing Risks Data

Usage

FreqID_HReg_R(
  Formula,
  data,
  na.action = "na.fail",
  subset = NULL,
  hazard = c("weibull"),
  frailty = TRUE,
  model,
  knots_list = NULL,
  penalty = c("scad", "mcp", "lasso"),
  lambda,
  a = NULL,
  mm_epsilon = 1e-08,
  penalty_fusedcoef = c("none", "fusedlasso"),
  lambda_fusedcoef = 0,
  penalty_fusedbaseline = c("none", "fusedlasso"),
  lambda_fusedbaseline = 0,
  penweights_list = list(),
  verbose = 0,
  startVals = NULL,
  optimization_method = "prox_grad",
  select_tol = 1e-04,
  fusion_tol = 0.001,
  maxit = 300,
  step_size_init = 1,
  step_size_min = 1e-06,
  step_size_max = 1e+06,
  step_size_scale = 0.5,
  conv_crit = "nll_pen_change",
  conv_tol = 1e-07,
  num_restarts = 2
)

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.
`lambda`	The strength of the parameterwise penalty. Either a single non-negative numeric value for all three transitions, or a length 3 vector with elements corresponding to the three transitions.
`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.
`penalty_fusedcoef`	A string value indicating the form of the fusion penalty to apply to the regression parameters. "none" and "fusedlasso" are the options.
`lambda_fusedcoef`	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.
`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"
`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.
`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 last`q_1+q_2+q_3` elements correspond with the regression parameters. If set to `NULL`, will be generated automatically using `get_start`.
`optimization_method`	vector of optimization methods to apply. Method achieving lowest objective function will be final reported result.
`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.
`maxit`	Positive integer maximum number of iterations.
`step_size_init`	Positive numeric value for the initial step size.
`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_scale`	Positive numeric value for the multiplicative change in step size at each step of backtracking.
`conv_crit`	String (possibly vector) giving the convergence criterion.
`conv_tol`	Positive numeric value giving the convergence tolerance for the chosen criterion.
`num_restarts`	Number of times to allow algorithm to restart if it reaches a point where it can make no further progress.