pencoxfrailControl: Control Values for 'pencoxfrail' fit

In PenCoxFrail: Regularization in Cox Frailty Models

Description

The values supplied in the function call replace the defaults and a list with all possible arguments is returned. The returned list is used as the control argument to the pencoxfrail function.

Usage

pencoxfrailControl(start = NULL, q_start = NULL, conv.eps = 1e-4, 
                          standardize = FALSE, center = FALSE,
                          smooth=list(nbasis = 6, penal = 0.1), 
                          ridge.pen = 1e-4, print.iter = FALSE, 
                          max.iter = 100, c.app = 1e-6, zeta = 0.5, 
                          exact = 1e-2, xr = NULL, ...)

Arguments

start	a vector of suitable length containing starting values for the spline-coefficients of the baseline hazard and the time-varying effects, followed by the fixed and random effects. The correct ordering is important. Default is a vector full of zeros.
q_start	a scalar or matrix of suitable dimension, specifying starting values for the random-effects variance-covariance matrix. Default is a scalar 0.1 or diagonal matrix with 0.1 in the diagonal, depending on the dimension of the random effects.
conv.eps	controls the speed of convergence. Default is 1e-4.
center	logical. If true, the covariates corresponding to the time-varying effects will be centered. Default is FALSE (and centering is only recommended if really necessary; it can also have a strong effect on the baseline hazard, in particular, if a strong penalty is selected).
standardize	logical. If true, the the covariates corresponding to the time-varying effects will be scaled to a variance equal to one (*after* possible centering). Default is FALSE.
smooth	a list specifying the number of basis functions nbasis (used for the baseline hazard and all time-varying effects) and the smoothness penalty parameter penal, which is only applied to the baseline hazard. All time-varying effects are penalized by the specific double-penalty ξ*J(ζ,α) (see pencoxfrail), which is based on the overall penalty parameter ξ (specified in the main function pencoxfrail) and on the weighting between the two penalty parts ζ. The degree of the B-splines is fixed to be three (i.e. cubic splines).
ridge.pen	On all time-varying effects (except for the baseline hazard) a slight ridge penalty is applied on the second order differences of the corresponding spline coefficients to stabilize estimation. Default is 1e-4.
print.iter	logical. Should the number of iterations be printed? Default is FALSE.
max.iter	the number of iterations for the final Fisher scoring re-estimation procedure. Default is 200.
c.app	The parameter controlling the exactness of the quadratic approximations of the penalties. Default is 1e-6.
zeta	The parameter controlling the weighting between the two penalty parts in the specific double-penalty ξ*J(ζ,α) (see pencoxfrail). Default is 0.5.
exact	controls the exactness of the (Riemann) integral approximations. Default is 1e-2.
xr	maximal time point that is regarded. Default is NULL and the maximal event or censoring time point in the data is used.
...	Futher arguments to be passed.

Value

a list with components for each of the possible arguments.

Author(s)

Andreas Groll [e-mail]

See Also

pencoxfrail

Examples

# Use different weighting of the two penalty parts
# and lighten the convergence criterion 
pencoxfrailControl(zeta=0.3, conv.eps=1e-3)

PenCoxFrail documentation built on May 2, 2019, 6:54 a.m.