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

In SemiCompRisks: Hierarchical Models for Parametric and Semi-Parametric Analyses of Semi-Competing Risks Data

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 β_1 for BayesID_HReg.
beta2	starting values of β_2 for BayesID_HReg.
beta3	starting values of β_3 for BayesID_HReg.
beta	starting values of β for BayesSurv_HReg.
gamma.ji	starting values of γ for BayesID_HReg.
theta	starting values of θ 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, α_g for BayesID_HReg. starting values of the Weibull parameter, α for BayesSurv_HReg.
WB.kappa	starting values of the Weibull parameters, κ_g for BayesID_HReg. starting values of the Weibull parameter, κ for BayesSurv_HReg.
PEM.lambda1	starting values of the PEM parameters, λ_1 for BayesID_HReg.
PEM.lambda2	starting values of the PEM parameters, λ_2 for BayesID_HReg.
PEM.lambda3	starting values of the PEM parameters, λ_3 for BayesID_HReg.
PEM.lambda	starting values of λ 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, μ_{λ,g} for BayesID_HReg. starting values of the PEM parameter, μ_{λ} for BayesSurv_HReg.
PEM.sigSq_lam	starting values of the PEM parameters, σ_{λ,g}^2 for BayesID_HReg. starting values of the PEM parameter, σ_{λ}^2 for BayesSurv_HReg.
MVN.SigmaV	starting values of Σ_V in DPM models for BayesID_HReg.
Normal.zeta	starting values of ζ 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 τ 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}}.

SemiCompRisks documentation built on Feb. 3, 2021, 5:06 p.m.