A fitted survPen object returned by function survPen and of class "survPen".
Method functions predict and summary are available for this class.
A survPen object has the following elements:
original survPen call
formula object specifying the model
name of the vector of origin times
name of the vector of follow-up times
name of the vector of right-censoring indicators
name of the vector of expected hazard
fitted hazard
estimated regression parameters. Unpenalized parameters are first, followed by the penalized ones
"net" for net survival estimation with penalized excess hazard model or "overall" for overall survival with penalized hazard model
degrees of freedom associated with fully parametric terms (unpenalized)
degrees of freedom associated with penalized terms
number of regression parameters
effective degrees of freedom
effective degrees of freedom corrected for smoothing parameter uncertainty
Akaike information criterion with number of parameters replaced by edf when there are penalized terms. Corresponds to 2*edf - 2*ll.unpen
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.
vector of numbers of iterations needed to estimate the regression parameters for each smoothing parameters trial. It thus contains iter.rho+1 elements.
design matrix of the model
penalty matrix of the model
vector of rescaling factors for the penalty matrices
Equivalent to pen but with every element multiplied by its associated smoothing parameter
List of penalty matrices associated with all "smf" calls
List of penalty matrices associated with all "tensor" calls
List of penalty matrices associated with all "tint" calls
List of penalty matrices associated with all "rd" calls
List of names for the "smf" calls associated with S.smf
List of names for the "tensor" calls associated with S.tensor
List of names for the "tint" calls associated with S.tint
List of names for the "rd" calls associated with S.rd
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
gradient vector of the log-likelihood with respect to the regression parameters
gradient vector of the penalized log-likelihood with respect to the regression parameters
hessian of the log-likelihood with respect to the regression parameters
hessian of the penalized log-likelihood with respect to the regression parameters
if TRUE, the hessian of the penalized log-likelihood has been perturbed at convergence
log-likelihood at convergence
penalized log-likelihood at convergence
transpose of the Jacobian of beta with respect to the log smoothing parameters
list containing the derivatives of the inverse of Hess with respect to the log smoothing parameters
list containing the derivatives of Hess.unpen with respect to the log smoothing parameters
estimated or given smoothing parameters
number of smoothing parameters
number of iterations needed to estimate the smoothing parameters
identify whether the smoothing parameters were estimated or not; 1 when exiting the function NR.rho; default is NULL
criterion used for smoothing parameter estimation
value of the criterion used for smoothing parameter estimation at convergence
Likelihood cross-validation criterion at convergence
negative Laplace approximate marginal likelihood at convergence
gradient vector of criterion with respect to the log smoothing parameters
hessian matrix of criterion with respect to the log smoothing parameters
inverse of Hess.rho
if TRUE, the hessian of LCV or LAML has been perturbed at convergence
Frequentist covariance matrix
Bayesian covariance matrix
Bayesian covariance matrix corrected for smoothing parameter uncertainty (see Wood et al. 2016)
Kass and Steffey approximation of Vc (see Wood et al. 2016)
List of matrices that represents the sum-to-zero constraint to apply for smf splines
List of matrices that represents the sum-to-zero constraint to apply for tensor splines
List of matrices that represents the sum-to-zero constraint to apply for tint splines
List of all smf.smooth.spec objects contained in the model
List of all tensor.smooth.spec objects contained in the model
List of all tint.smooth.spec objects contained in the model
List of all rd.smooth.spec objects contained in the model
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
List containing the levels and classes of all factor variables present in the data frame used for fitting
convergence indicator, TRUE or FALSE. TRUE if Hess.beta.modif=FALSE and Hess.rho.modif=FALSE (or NULL)
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