initiate.startValues_HReg: The function that initiates starting values for a single chain.

Description

The function initiates starting values for a single chain for hazard regression (HReg) models. Users are allowed to set some non-null values to starting values for a set of parameters. The function will automatically generate starting values for any parameters whose values are not specified.

Usage

initiate.startValues_HReg(Formula, data, model, id = NULL, nChain=1,
                   beta1 = NULL, beta2 = NULL, beta3 = NULL, beta = NULL,
                   gamma.ji = NULL, theta = NULL,
                   V.j1 = NULL, V.j2 = NULL, V.j3 = NULL, V.j = NULL,
                   WB.alpha = NULL, WB.kappa = NULL, 
                   PEM.lambda1=NULL, PEM.lambda2=NULL, PEM.lambda3=NULL, PEM.lambda=NULL,
                   PEM.s1=NULL, PEM.s2=NULL, PEM.s3=NULL, PEM.s=NULL,
                   PEM.mu_lam=NULL, PEM.sigSq_lam=NULL,
                   MVN.SigmaV = NULL, Normal.zeta = NULL, 
                   DPM.class = NULL, DPM.tau = NULL)

Arguments

Formula

For BayesID_HReg, it is a data.frame containing semi-competing risks outcomes from n subjects. For BayesSurv_HReg, it is a data.frame containing univariate time-to-event outcomes from n subjects. For BayesID_HReg, it is a list containing three formula objects that correspond to \(h_g()\), \(g\)=1,2,3. For BayesSurv_HReg, it is a formula object that corresponds to \(h()\).

data

a data.frame in which to interpret the variables named in the formula(s) in lin.pred.

model

a character vector that specifies the type of components in a model. Check BayesID_HReg and BayesSurv_HReg.

a vector of cluster information for n subjects. The cluster membership must be set to consecutive positive integers, \(1:J\).

nChain

The number of chains.

beta1

starting values of \(\beta_1\) for BayesID_HReg.

beta2

starting values of \(\beta_2\) for BayesID_HReg.

beta3

starting values of \(\beta_3\) for BayesID_HReg.

beta

starting values of \(\beta\) for BayesSurv_HReg.

gamma.ji

starting values of \(\gamma\) for BayesID_HReg.

theta

starting values of \(\theta\) for BayesID_HReg.

V.j1

starting values of \(V_{j1}\) for BayesID_HReg.

V.j2

starting values of \(V_{j2}\) for BayesID_HReg.

V.j3

starting values of \(V_{j3}\) for BayesID_HReg.

V.j

starting values of \(V_{j}\) for BayesSurv_HReg.

WB.alpha

starting values of the Weibull parameters, \(\alpha_g\) for BayesID_HReg. starting values of the Weibull parameter, \(\alpha\) for BayesSurv_HReg.

WB.kappa

starting values of the Weibull parameters, \(\kappa_g\) for BayesID_HReg. starting values of the Weibull parameter, \(\kappa\) for BayesSurv_HReg.

PEM.lambda1

starting values of the PEM parameters, \(\lambda_1\) for BayesID_HReg.

PEM.lambda2

starting values of the PEM parameters, \(\lambda_2\) for BayesID_HReg.

PEM.lambda3

starting values of the PEM parameters, \(\lambda_3\) for BayesID_HReg.

PEM.lambda

starting values of \(\lambda\) for BayesSurv_HReg.

PEM.s1

starting values of the PEM parameters, \(s_1\) for BayesID_HReg.

PEM.s2

starting values of the PEM parameters, \(s_2\) for BayesID_HReg.

PEM.s3

starting values of the PEM parameters, \(s_3\) for BayesID_HReg.

PEM.s

starting values of \(s\) for BayesSurv_HReg.

PEM.mu_lam

starting values of the PEM parameters, \(\mu_{\lambda,g}\) for BayesID_HReg. starting values of the PEM parameter, \(\mu_{\lambda}\) for BayesSurv_HReg.

PEM.sigSq_lam

starting values of the PEM parameters, \(\sigma_{\lambda,g}^2\) for BayesID_HReg. starting values of the PEM parameter, \(\sigma_{\lambda}^2\) for BayesSurv_HReg.

MVN.SigmaV

starting values of \(\Sigma_V\) in DPM models for BayesID_HReg.

Normal.zeta

starting values of \(\zeta\) in DPM models for BayesSurv_HReg.

DPM.class

starting values of the class membership in DPM models for BayesID_HReg and BayesSurv_HReg.

DPM.tau

starting values of \(\tau\) in DPM models for BayesID_HReg and BayesSurv_HReg.

Value

initiate.startValues_HReg returns a list containing starting values for a sigle chain that can be used for BayesID_HReg and BayesSurv_HReg.

References

Lee, K. H., Haneuse, S., Schrag, D., and Dominici, F. (2015), Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis, Journal of the Royal Statistical Society: Series C, 64, 2, 253-273. Lee, K. H., Dominici, F., Schrag, D., and Haneuse, S. (2016), Hierarchical models for semicompeting risks data with application to quality of end-of-life care for pancreatic cancer, Journal of the American Statistical Association, 111, 515, 1075-1095. Alvares, D., Haneuse, S., Lee, C., Lee, K. H. (2019), SemiCompRisks: An R package for the analysis of independent and cluster-correlated semi-competing risks data, The R Journal, 11, 1, 376-400.

Examples

Run this code

# NOT RUN {
## See Examples in \code{\link{BayesID_HReg}} and \code{\link{BayesSurv_HReg}}.
# }