coxlassoControl: Control Values for 'coxlasso' fit

In PenCoxFrail: Regularization in Cox Frailty Models

Control Values for coxlasso fit

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 coxlasso function.

Usage

coxlassoControl(start = NULL, index=NULL, q_start = NULL, conv.eps = 1e-3, 
                          standardize = TRUE, center = FALSE,
                          smooth=list(nbasis = 6, penal = 1e+2), 
                          print.iter = FALSE, max.iter = 100, c.app = 1e-6,  
                          exact = NULL, xr = NULL, eps = 1e-2, quant.knots = TRUE,...)

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.
index	vector which defines the grouping of the variables. Components sharing the same number build a group and factor variables get a single number (and are automatically treated as a group). Non-penalized coefficients are marked with NA.
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-3.
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 covariates corresponding to the fixed effects will be scaled to a variance equal to one. Default is TRUE.
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 \xi\cdot J(\zeta,\alpha) (see coxlasso), which is based on the overall penalty parameter \xi (specified in the main function coxlasso) and on the weighting between the two penalty parts \zeta. The degree of the B-splines is fixed to be three (i.e. cubic splines).
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.
exact	controls the exactness of the (Riemann) integral approximations. If not set by the user to a specific value, it will be automatically chosen such that the time interval [0,t_max] will be divided in about 1000 equal sized Riemann bars.
xr	maximal time point that is regarded. Default is NULL and the maximal event or censoring time point in the data is used.
eps	Small epsilon that controls which fixed effects are set to zero: parameters with an absolute value smaller than epsilon are taken to be zero. Default is 1e-2.
quant.knots	Shall the knots be defined based on quantiles? Default is TRUE.

.

...	Futher arguments to be passed.

Value

a list with components for each of the possible arguments.

Author(s)

Andreas Groll [e-mail]

See Also

coxlasso

Examples

# Use different weighting of the two penalty parts
# and lighten the convergence criterion 
coxlassoControl(c.app = 1e-5, conv.eps=1e-3)

PenCoxFrail documentation built on Sept. 11, 2024, 7:12 p.m.