sshzd2d: Estimating 2-D Hazard Function Using Smoothing Splines
In gsscopu: Copula Density and 2-D Hazard Estimation using Smoothing Splines

Description Usage Arguments Details Value Note Author(s) References Examples

View source: R/sshzd2d.R

Estimate 2-D hazard function using smoothing spline ANOVA models.

sshzd2d(formula1, formula2, symmetry=FALSE, data, alpha=1.4,
        weights=NULL, subset=NULL, id.basis=NULL, nbasis=NULL, seed=NULL,
        prec=1e-7, maxiter=30, skip.iter=FALSE)

sshzd2d1(formula1, formula2, symmetry=FALSE, data, alpha=1.4,
         weights=NULL, subset=NULL, rho="marginal",
         id.basis=NULL, nbasis=NULL, seed=NULL, prec=1e-7, maxiter=30,
         skip.iter=FALSE)

`formula1`	Description of the hazard model to be fit on the first axis.
`formula2`	Description of the hazard model to be fit on the second axis.
`symmetry`	Flag indicating whether to enforce symmetry of the two axes.
`data`	Data frame containing the variables in the model.
`alpha`	Parameter defining cross-validation scores for smoothing parameter selection.
`weights`	Optional vector of counts for duplicated data.
`subset`	Optional vector specifying a subset of observations to be used in the fitting process.
`id.basis`	Index of observations to be used as "knots."
`nbasis`	Number of "knots" to be used. Ignored when `id.basis` is specified.
`seed`	Seed to be used for the random generation of "knots." Ignored when `id.basis` is specified.
`prec`	Precision requirement for internal iterations.
`maxiter`	Maximum number of iterations allowed for internal iterations.
`skip.iter`	Flag indicating whether to use initial values of theta and skip theta iteration in marginal hazard estimation.
`rho`	Choice of rho function for sshzd2d1: `"marginal"` or `"weibull"`.

The 2-D survival function is expressed as S(t1,t2)=C(S1(t1),S2(t2)), where S1(t1), S2(t2) are marginal survival functions and C(u1,u2) is a 2-D copula. The marginal survival functions are estimated via the marginal hazards as in sshzd, and the copula is estimated nonparametrically by calling sscopu2.

When symmetry=TRUE, a common marginal survial function S1(t)=S2(t) is estimated, and a symmetric copula is estimated such that C(u1,u2)=C(u2,u1).

Covariates can be incorporated in the marginal hazard models as in sshzd, including parametric terms via partial and frailty terms via random. Arguments formula1 and formula2 are typically model formulas of the same form as the argument formula in sshzd, but when partial or random are needed, formula1 and formula2 should be lists with model formulas as the first elements and partial/random as named elements; when necessary, variable configurations (that are done via argument type in sshzd) should also be entered as named elements of lists formula1/formula2.

When symmetry=TRUE, parallel model formulas must be consistent of each other, such as

formula1=list(Surv(t1,d1)~t1*u1,partial=~z1,random=~1|id1)

formula2=list(Surv(t2,d2)~t2*u2,partial=~z2,random=~1|id2)

where pairs t1-t2, d2-d2 respectively are different elements in data, pairs u1-u2, z1-z2 respectively may or may not be different elements in data, and factors id1 and id2 are typically the same but at least should have the same levels.

sshzd2d and sshzd2d1 return a list object of class "sshzd2d".

hzdrate.sshzd2d can be used to evaluate the estimated 2-D hazard function. survexp.sshzd2d can be used to calculate estimated survival functions.

sshzd2d1 executes faster than sshzd2d, but often at the cost of performance degradation.

The results may vary from run to run. For consistency, specify id.basis or set seed.

Chong Gu, chong@stat.purdue.edu

Gu, C. (2013), Hazard estimation with bivariate survival data and copula density estimation.

## THE FOLLOWING EXAMPLE IS TIME-CONSUMING
## Not run: 
data(DiaRet)
## Common proportional hazard model on the margins
fit <- sshzd2d(Surv(time1,status1)~time1+trt1*type,
               Surv(time2,status2)~time2+trt2*type,
               data=DiaRet,symmetry=TRUE)
## Evaluate fitted survival and hazard functions
time <- cbind(c(50,70),c(70,70))
cova <- data.frame(trt1=as.factor(c(1,1)),trt2=as.factor(c(1,0)),
                   type=as.factor(c("juvenile","adult")))
survexp.sshzd2d(fit,time,cov=cova)
hzdrate.sshzd2d(fit,time,cov=cova)
## Association between margins: Kendall's tau and Spearman's rho
summary(fit$copu)
## Clean up
rm(DiaRet,fit,time,cova)
dev.off()

## End(Not run)