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

Description

The function initiates starting values for a single chain for accelrated failture time (AFT) 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_AFT(Formula, data, model, nChain=1,
                            beta1=NULL, beta2=NULL, beta3=NULL, beta=NULL,
                            gamma=NULL, theta=NULL,
                            y1=NULL, y2=NULL, y=NULL,
                            LN.mu=NULL, LN.sigSq=NULL,
                            DPM.class1=NULL, DPM.class2=NULL, DPM.class3=NULL,
                            DPM.class=NULL, DPM.mu1=NULL, DPM.mu2=NULL,
                            DPM.mu3=NULL, DPM.mu=NULL, DPM.zeta1=NULL,
                            DPM.zeta2=NULL, DPM.zeta3=NULL, DPM.zeta=NULL,
                            DPM.tau=NULL)

Arguments

Formula

For BayesID_AFT, it is a data.frame containing semi-competing risks outcomes from n subjects. See BayesID_AFT. For BayesSurv_AFT, it is a data.frame containing univariate time-to-event outcomes from n subjects. See BayesSurv_AFT. For BayesID_AFT, it is a list containing three formula objects that correspond to the transition \(g\)=1,2,3. For BayesSurv_AFT, it is a formula object that corresponds to \(log(t)\).

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_AFT and BayesSurv_AFT.

nChain

The number of chains.

beta1

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

beta2

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

beta3

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

beta

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

gamma

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

theta

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

starting values of \(log(t_1)\) for BayesID_AFT.

starting values of \(log(t_2)\) for BayesID_AFT.

starting values of \(log(t)\) for BayesSurv_AFT.

LN.mu

starting values of \(\beta_0\) in logNormal models for BayesID_AFT and BayesSurv_AFT.

LN.sigSq

starting values of \(\sigma^2\) in logNormal models for BayesID_AFT and BayesSurv_AFT.

DPM.class1

starting values of the class membership for transition 1 in DPM models for BayesID_AFT.

DPM.class2

starting values of the class membership for transition 2 in DPM models for BayesID_AFT.

DPM.class3

starting values of the class membership for transition 3 in DPM models for BayesID_AFT.

DPM.class

starting values of the class membership in DPM models for BayesSurv_AFT.

DPM.mu1

starting values of \(\mu_1\) in DPM models for BayesID_AFT.

DPM.mu2

starting values of \(\mu_2\) in DPM models for BayesID_AFT.

DPM.mu3

starting values of \(\mu_3\) in DPM models for BayesID_AFT.

DPM.mu

starting values of \(\mu\) in DPM models for BayesSurv_AFT.

DPM.zeta1

starting values of \(\zeta_{1}\) in DPM models for BayesID_AFT.

DPM.zeta2

starting values of \(\zeta_{2}\) in DPM models for BayesID_AFT.

DPM.zeta3

starting values of \(\zeta_{3}\) in DPM models for BayesID_AFT.

DPM.zeta

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

DPM.tau

starting values of \(\tau\) in DPM models for BayesID_AFT and BayesSurv_AFT.

Value

initiate.startValues_AFT returns a list containing starting values for a sigle chain that can be used for BayesID_AFT and BayesSurv_AFT.

References

Lee, K. H., Rondeau, V., and Haneuse, S. (2017), Accelerated failure time models for semicompeting risks data in the presence of complex censoring, Biometrics, 73, 4, 1401-1412. 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_AFT}} and \code{\link{BayesSurv_AFT}}.
# }