survPenObject: Fitted survPen object

survPenObjectR Documentation

Fitted survPen object

Description

A fitted survPen object returned by function survPen and of class "survPen". Method functions predict and summary are available for this class.

Value

A survPen object has the following elements:

call

original survPen call

formula

formula object specifying the model

t0.name

name of the vector of origin times

t1.name

name of the vector of follow-up times

event.name

name of the vector of right-censoring indicators

expected.name

name of the vector of expected hazard

haz

fitted hazard

coefficients

estimated regression parameters. Unpenalized parameters are first, followed by the penalized ones

type

"net" for net survival estimation with penalized excess hazard model or "overall" for overall survival with penalized hazard model

df.para

degrees of freedom associated with fully parametric terms (unpenalized)

df.smooth

degrees of freedom associated with penalized terms

p

number of regression parameters

edf

effective degrees of freedom

edf1

alternative effective degrees of freedom ; used as an upper bound for edf2

edf2

effective degrees of freedom corrected for smoothing parameter uncertainty

aic

Akaike information criterion with number of parameters replaced by edf when there are penalized terms. Corresponds to 2*edf - 2*ll.unpen

aic2

Akaike information criterion corrected for smoothing parameter uncertainty. Be careful though, this is still a work in progress, especially when one of the smoothing parameters tends to infinity.

iter.beta

vector of numbers of iterations needed to estimate the regression parameters for each smoothing parameters trial. It thus contains iter.rho+1 elements.

X

design matrix of the model

S

penalty matrix of the model

S.scale

vector of rescaling factors for the penalty matrices

S.list

Equivalent to pen but with every element multiplied by its associated smoothing parameter

S.smf

List of penalty matrices associated with all "smf" calls

S.tensor

List of penalty matrices associated with all "tensor" calls

S.tint

List of penalty matrices associated with all "tint" calls

S.rd

List of penalty matrices associated with all "rd" calls

smooth.name.smf

List of names for the "smf" calls associated with S.smf

smooth.name.tensor

List of names for the "tensor" calls associated with S.tensor

smooth.name.tint

List of names for the "tint" calls associated with S.tint

smooth.name.rd

List of names for the "rd" calls associated with S.rd

S.pen

List of all the rescaled penalty matrices redimensioned to df.tot size. Every element of S.pen noted S.pen[[i]] is made from a penalty matrix pen[[i]] returned by smooth.cons and is multiplied by S.scale

grad.unpen.beta

gradient vector of the log-likelihood with respect to the regression parameters

grad.beta

gradient vector of the penalized log-likelihood with respect to the regression parameters

Hess.unpen.beta

hessian of the log-likelihood with respect to the regression parameters

Hess.beta

hessian of the penalized log-likelihood with respect to the regression parameters

Hess.beta.modif

if TRUE, the hessian of the penalized log-likelihood has been perturbed at convergence

ll.unpen

log-likelihood at convergence

ll.pen

penalized log-likelihood at convergence

deriv.rho.beta

transpose of the Jacobian of beta with respect to the log smoothing parameters

deriv.rho.inv.Hess.beta

list containing the derivatives of the inverse of Hess with respect to the log smoothing parameters

deriv.rho.Hess.unpen.beta

list containing the derivatives of Hess.unpen with respect to the log smoothing parameters

lambda

estimated or given smoothing parameters

nb.smooth

number of smoothing parameters

iter.rho

number of iterations needed to estimate the smoothing parameters

optim.rho

identify whether the smoothing parameters were estimated or not; 1 when exiting the function NR.rho; default is NULL

method

criterion used for smoothing parameter estimation

criterion.val

value of the criterion used for smoothing parameter estimation at convergence

LCV

Likelihood cross-validation criterion at convergence

LAML

negative Laplace approximate marginal likelihood at convergence

grad.rho

gradient vector of criterion with respect to the log smoothing parameters

Hess.rho

hessian matrix of criterion with respect to the log smoothing parameters

inv.Hess.rho

inverse of Hess.rho

Hess.rho.modif

if TRUE, the hessian of LCV or LAML has been perturbed at convergence

Ve

Frequentist covariance matrix

Vp

Bayesian covariance matrix

Vc

Bayesian covariance matrix corrected for smoothing parameter uncertainty (see Wood et al. 2016)

Vc.approx

Kass and Steffey approximation of Vc (see Wood et al. 2016)

Z.smf

List of matrices that represents the sum-to-zero constraint to apply for smf splines

Z.tensor

List of matrices that represents the sum-to-zero constraint to apply for tensor splines

Z.tint

List of matrices that represents the sum-to-zero constraint to apply for tint splines

list.smf

List of all smf.smooth.spec objects contained in the model

list.tensor

List of all tensor.smooth.spec objects contained in the model

list.tint

List of all tint.smooth.spec objects contained in the model

list.rd

List of all rd.smooth.spec objects contained in the model

U.F

Eigen vectors of S.F, useful for the initial reparameterization to separate penalized ad unpenalized subvectors. Allows stable evaluation of the log determinant of S and its derivatives

is.pwcst

TRUE if there is a piecewise constant (excess) hazard specification. In that case the cumulative hazard can be derived without Gauss-Legendre quadrature

pwcst.breaks

if is.pwcst is TRUE, vector of breaks defining the sub-intervals on which the hazard is constant. Otherwise NULL.

factor.structure

List containing the levels and classes of all factor variables present in the data frame used for fitting

converged

convergence indicator, TRUE or FALSE. TRUE if Hess.beta.modif=FALSE and Hess.rho.modif=FALSE (or NULL)

References

Wood, S.N., Pya, N. and Saefken, B. (2016), Smoothing parameter and model selection for general smooth models (with discussion). Journal of the American Statistical Association 111, 1548-1575


survPen documentation built on Sept. 14, 2023, 1:06 a.m.