PiecewiseBayesSelect {BayesPieceHazSelect} | R Documentation |
PiecewiseBayesSelect
PiecewiseBayesSelect(Y1, I1, X, hyperparameters, beta1start, B, inc, Path, burn)
Y1 |
Vector Containing event times (or censoring time due to death/censoring) |
I1 |
Vector Containing event indicators (1 if l event for a patient, 0 otherwise) |
X |
Matrix of Patient Covariates, the last inc are left out of the selection procedure |
hyperparameters |
List containing 11 hyperparameters and four starting values. In order they are: psi-the swap rate of the SVSS algorithm. c-parameter involved in Sigma matrix for selection. z1a, z1b - beta hyper parameters on probability of inclusion for each of the three hazard functions. a1,b1- hyperparameters on sigma_lambda. clam1- spatial dependency of baseline hazard (between 0 and 1) for the hazard function. Alpha1 - The parameter for the number of split points in the hazard (must be whole number). J1max - Maximum number of split points allowed (must be whole number). J1- Starting number of split points. cl1 -Tuning parameter for log baseline hazard height sampler. |
beta1start |
Starting Values for Beta1 |
B |
Number of iterations |
inc |
Number of variables left out of selection |
Path |
Where to save posterior samples |
burn |
percent of posterior sample to burn in (burn*B must be a whole number) |
##Randomly Generate Semicompeting Risks Data ####Generates random patient time, indicator and covariates. n=100 Y1=runif(n,0,100) I1=rbinom(n,1,.5) library(mvtnorm) X=rmvnorm(n,rep(0,13),diag(13)) ####Read in Hyperparameters ##Swap Rate psi=.5 c=20 ###Eta Beta function probabilities z1a=.4 z1b=1.6 ####Hierarchical lam params ###Sigma^2 lambda_ hyperparameters a1=.7 b1=.7 ##Spacing dependence c in [0,1] clam1=1 #####NumSplit alpha1=3 J1max=10 ####Split Point Starting Value ### J1=3 ##Tuning parameter for lambda cl1=.25 ###Beta Starting Values beta1start=c(0,0,-1,0,0,0,1,1,1,1,1,-1,-1) hyper=c(psi,c,z1a,z1b,a1,b1,clam1,alpha1,J1max,J1,cl1) ###Number of iterations and output location B=200 Path=tempdir() inc=2 burn=.4 PiecewiseBayesSelect(Y1,I1,X,hyper,beta1start,B,inc,Path,burn)