drkpk {gss}R Documentation

Numerical Engine for ssden, sshzd, and sshzd1

Description

Perform numerical calculations for the ssden and sshzd suites.

Usage

sspdsty(s, r, q, cnt, qd.s, qd.r, qd.wt, prec, maxiter, alpha)
mspdsty(s, r, id.basis, cnt, qd.s, qd.r, qd.wt, prec, maxiter, alpha, skip.iter)

msphzd(s, r, id.wk, Nobs, cnt, qd.s, qd.r, qd.wt, prec, maxiter, alpha, skip.iter)

msphzd1(s, r, id.wk, Nobs, cnt, int.s, int.r, rho, prec, maxiter, alpha, skip.iter)

mspllrm(s, r, id.basis, cnt, qd.s, qd.r, qd.wt, id.x, prec, maxiter, alpha, skip.iter)

Arguments

s Unpenalized terms evaluated at data points.
r Basis of penalized terms evaluated at data points.
q Penalty matrix.
id.basis Index of observations to be used as "knots."
id.wk Index of observations to be used as "knots."
Nobs Total number of lifetime observations.
cnt Bin-counts for histogram data.
qd.s Unpenalized terms evaluated at quadrature nodes.
qd.r Basis of penalized terms evaluated at quadrature nodes.
qd.wt Quadrature weights.
prec Precision requirement for internal iterations.
maxiter Maximum number of iterations allowed for internal iterations.
alpha Parameter defining cross-validation score for smoothing parameter selection.
skip.iter Flag indicating whether to use initial values of theta and skip theta iteration.
int.s Integrals of unpenalized terms.
int.r Integrals of basis of penalized terms.
rho rho function value on failure times.
id.x position of x value in list of unique x.

Details

sspdsty is used by ssden to compute cross-validated density estimate with a single smoothing parameter. mspdsty is used by ssden to compute cross-validated density estimate with multiple smoothing parameters.

msphzd is used by sshzd to compute cross-validated hazard estimate with single or multiple smoothing parameters.

References

Gu, C. (2002), Smoothing Spline ANOVA Models. New York: Springer-Verlag.

Gu, C. and Wang, J. (2003), Penalized likelihood density estimation: Direct cross-validation and scalable approximation. Statistica Sinica, 13, 811–826.

Du, P. and Gu, C. (2006), Penalized likelihood hazard estimation: efficient approximation and Bayesian confidence intervals. Statistics and Probability Letters, 76, 244–254.

Du, P. and Gu, C. (2008), Penalized Pseudo-Likelihood Hazard Estimation: A Fast Alternative to Penalized Likelihood. Journal of Statistical Planning and Inference, 00, 000–000.


[Package gss version 1.1-0 Index]