initiate.startValues_HReg {SemiCompRisks}

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.

id

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.

Author(s)

Sebastien Haneuse and Kyu Ha Lee
Maintainer: Kyu Ha Lee <[e-mail]>

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.

See Also

BayesID_HReg, BayesSurv_HReg

Examples

## See Examples in \code{\link{BayesID_HReg}} and \code{\link{BayesSurv_HReg}}.