SemiCompRisks (version 2.4)

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

Description

The function initiates starting values for a single chain. 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(Y, lin.pred, data, model, cluster = NULL,  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

Y

For BayesID, it is a data.frame containing semi-competing risks outcomes from n subjects. For BayesSurv, it is a data.frame containing univariate time-to-event outcomes from n subjects.

lin.pred

For BayesID, it is a list containing three formula objects that correspond to $h_g()$, $g$=1,2,3. For BayesSurv, 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 and BayesSurv.

cluster

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

beta1

starting values of $\beta_1$ for BayesID.

beta2

starting values of $\beta_2$ for BayesID.

beta3

starting values of $\beta_3$ for BayesID.

beta

starting values of $\beta$ for BayesSurv.

gamma.ji

starting values of $\gamma$ for BayesID.

theta

starting values of $\theta$ for BayesID.

V.j1

starting values of $V_{j1}$ for BayesID.

V.j2

starting values of $V_{j2}$ for BayesID.

V.j3

starting values of $V_{j3}$ for BayesID.

V.j

starting values of $V_{j}$ for BayesSurv.

WB.alpha

starting values of the Weibull parameters, $\alpha_g$ for BayesID. starting values of the Weibull parameter, $\alpha$ for BayesSurv.

WB.kappa

starting values of the Weibull parameters, $\kappa_g$ for BayesID. starting values of the Weibull parameter, $\kappa$ for BayesSurv.

PEM.lambda1

starting values of the PEM parameters, $\lambda_1$ for BayesID.

PEM.lambda2

starting values of the PEM parameters, $\lambda_2$ for BayesID.

PEM.lambda3

starting values of the PEM parameters, $\lambda_3$ for BayesID.

PEM.lambda

starting values of $\lambda$ for BayesSurv.

PEM.s1

starting values of the PEM parameters, $s_1$ for BayesID.

PEM.s2

starting values of the PEM parameters, $s_2$ for BayesID.

PEM.s3

starting values of the PEM parameters, $s_3$ for BayesID.

PEM.s

starting values of $s$ for BayesSurv.

PEM.mu_lam

starting values of the PEM parameters, $\mu_{\lambda,g}$ for BayesID. starting values of the PEM parameter, $\mu_{\lambda}$ for BayesSurv.

PEM.sigSq_lam

starting values of the PEM parameters, $\sigma_{\lambda,g}^2$ for BayesID. starting values of the PEM parameter, $\sigma_{\lambda}^2$ for BayesSurv.

MVN.SigmaV

starting values of $\Sigma_V$ in DPM models for BayesID.

Normal.zeta

starting values of $\zeta$ in DPM models for BayesSurv.

DPM.class

starting values of the class membership in DPM models for BayesID and BayesSurv.

DPM.tau

starting values of $\tau$ in DPM models for BayesID and BayesSurv.

Value

initiate.startValues returns a list containing starting values for a sigle chain that can be used for BayesID and BayesSurv.

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, in press.

See Also

BayesID, BayesSurv

Examples

## See Examples in \code{\link{BayesID}} and \code{\link{BayesSurv}}.