The gss Package - NexTag Supports Open Source Initiatives

44 sshzd
Value
Note
The selection of smoothing parameters is through a cross-validation mechanism described in Gu
(2002, Sec. 7.2), with a parameter alpha; alpha=1 is "unbiased" for the minimization of
Kullback-Leibler loss but may yield severe undersmoothing, whereas larger alpha yields smoother
estimates.
A subset of the observations are selected as "knots." Unless specified via id.basis or nbasis,
the number of "knots" q is determined by max(30, 10n 2/9 ), which is appropriate for the default
cubic splines for numerical vectors.
sshzd returns a list object of class "sshzd".
hzdrate.sshzd can be used to evaluate the estimated hazard function. hzdcurve.sshzd
can be used to evaluate hazard curves with fixed covariates. survexp.sshzd can be used to
calculated estimated expected survival. The method project.sshzd can be used to calculate
the Kullback-Leibler projection for model selection.
The function Surv(futime,status,start=0) is defined and parsed inside sshzd, not
quite the same as the one in the survival package.
Integration on the time axis is done by the 200-point Gauss-Legendre formula on c(min(start),max(futime)),
returned from gauss.quad.
The results may vary from run to run. For consistency, specify id.basis or set seed.
Author(s)
Chong Gu, 〈chong@stat.purdue.edu〉
References
Gu, C. (2002), Smoothing Spline ANOVA Models. New York: Springer-Verlag.
Du, P. and Gu, C. (2006), Penalized likelihood hazard estimation: efficient approximation and
Bayesian confidence intervals. Statistics and Probability Letters, 76, 244–254.
Examples
## Model with interaction
data(gastric)
gastric.fit