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

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/initiate.startValues_AFT.R

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

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    initiate.startValues_AFT(Y, lin.pred, data, model,
                            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

Y

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.

lin.pred

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.

beta1

starting values of β_1 for BayesID_AFT.

beta2

starting values of β_2 for BayesID_AFT.

beta3

starting values of β_3 for BayesID_AFT.

beta

starting values of β for BayesSurv_AFT.

gamma

starting values of γ for BayesID_AFT.

theta

starting values of θ for BayesID_AFT.

y1

starting values of log(t_1) for BayesID_AFT.

y2

starting values of log(t_2) for BayesID_AFT.

y

starting values of log(t) for BayesSurv_AFT.

LN.mu

starting values of β_0 in logNormal models for BayesID_AFT and BayesSurv_AFT.

LN.sigSq

starting values of σ^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 μ_1 in DPM models for BayesID_AFT.

DPM.mu2

starting values of μ_2 in DPM models for BayesID_AFT.

DPM.mu3

starting values of μ_3 in DPM models for BayesID_AFT.

DPM.mu

starting values of μ in DPM models for BayesSurv_AFT.

DPM.zeta1

starting values of ζ_{1} in DPM models for BayesID_AFT.

DPM.zeta2

starting values of ζ_{2} in DPM models for BayesID_AFT.

DPM.zeta3

starting values of ζ_{3} in DPM models for BayesID_AFT.

DPM.zeta

starting values of ζ in DPM models for BayesSurv_AFT.

DPM.tau

starting values of τ 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.

Author(s)

Sebastien Haneuse and Kyu Ha Lee
Maintainer: Kyu Ha Lee <[email protected]>

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.

See Also

BayesID_AFT, BayesSurv_AFT

Examples

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## See Examples in \code{\link{BayesID_AFT}} and \code{\link{BayesSurv_AFT}}.

SemiCompRisks documentation built on Jan. 3, 2018, 10:50 p.m.