SCRSELECTRETURN: Performs Bayesian Variable Selection on the covariates in a...

Description Usage Arguments Value References Examples

View source: R/SCRSELECTRETURN.R

Description

Performs Bayesian Variable Selection on the covariates in a semi-competing risks model and returns burned in posterior means of parameters. This function is used in the ReturnModel function.

Usage

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SCRSELECTRETURN(Y1, I1, Y2, I2, X, hyperparameters, beta1start, beta2start,
  beta3start, B, inc, burn)

Arguments

Y1

Vector Containing non-terminal event times (or censoring time due to death/censoring)

I1

Vector Containing non-terminal event indicators (1 if non-terminal event for a patient, 0 otherwise)

Y2

Vector Containing Terminal Event times (or censoring)

I2

Vector Containing Terminal event indicators (1 if a patients experiences a non-ternminal event, 0 if censored)

X

Matrix of Patient Covariates. The last inc will be left out of variable selection.

hyperparameters

List containing 29 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, z2a, z2b, z3a, z3b - beta hyper parameters on probability of inclusion for each of the three hazard functions. a1,b1,a2,b2,a3,b3- hyperparameters on sigma_lambda_1, sigma_lambda_2, and sigma_lambda_3. clam1, clam2, clam3 - spatial dependency of baseline hazard (between 0 and 1) for the three hazard functions. Alpha1, Alpha2, Alpha3 - The parameter for the number of split points in hazards 1,2 and 3 (must be whole number). J1max, J2max, J3max - Maximum number of split points allowed (must be whole number). J1, J2, J3- Starting number of split points. w, psi1- hyperparameters on theta^-1. cep=Tuning Parameter for theta^-1 sampler. epstart-Starting value for theta^-1. cl1,cl2,cl3-Tuning parameters for log baseline hazard height sampler.

beta1start

Starting Values for Beta1

beta2start

Starting Values for Beta2

beta3start

Starting Values for Beta3

B

Number of iterations

inc

Number of variables left out of selection

burn

percent of posterior sample to burn in (burn*B must be a whole number)

Value

Returns a list the following posterior quantities after burn in: Marginal probability of inclusion, mean frailty parameters, and the baseline hazard samples for each hazard.

References

[1] 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 (Applied Statistics), 64: 253-273. doi: 10.1111/rssc.12078

Examples

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####Randomly Generate Semicompeting Risks Data
set.seed(1)
####Generates random patient time, indicator and covariates.
n=100
Y1=runif(n,0,100)
I1=rbinom(n,1,.5)
Y2=Y1
I2=I1
for(i in 1:n){if(I1[i]==0){Y2[i]=Y1[i]}else{Y2[i]=Y1[i]+runif(1,0,100)}}
I2=rbinom(n,1,.5)
library(mvtnorm)
X=rmvnorm(n,rep(0,7),diag(7))
####Read in Hyperparameters
##Swap Rate
psi=.5
c=5
###Eta Beta function probabilities
z1a=.4
z1b=1.6
z2a=.4
z2b=1.6
z3a=.4
z3b=1.6
####Hierarchical lam params
###Sigma^2 lambda_g hyperparameters
a1=.7
b1=.7
a2=a1
b2=b1
a3=a1
b3=b1
##Spacing dependence c in [0,1]
clam1=1
clam2=1
clam3=1
#####NumSplit
alpha1=3
alpha2=3
alpha3=3
J1max=10
J2max=10
J3max=10
####Split Point Starting Value ###
J1=3
J2=3
J3=3
###epsilon starting values/hyperparameters###
w=.7
psi1=.7
cep=2.4
#############
epstart=1.5
cl1=.25
cl2=.25
cl3=.25
###Beta Starting Values
beta1start=c(1,1,1,1,1,-1,-1)
beta2start=c(1,1,1,1,1,-1,-1)
beta3start=c(-1,1,1,1,1,-1,-1)
hyper1=c(psi,c,z1a,z1b,z2a,z2b,z3a,z3b,a1,b1,a2,b2,a3,b3,clam1,clam2,clam3)
hyper2=c(alpha1,alpha2,alpha3,J1max,J2max,J3max,J1,J2,J3,w,psi1,cep,epstart,cl1,cl2,cl3)
hyper=c(hyper1,hyper2)
###Number of iterations and output location
B=100
Path=tempdir()
###Number of variables to exclude from selection and burnin percent
inc=2
burn=.1
SCRSELECTRETURN(Y1,I1,Y2,I2,X,hyper,beta1start,beta2start,beta3start,B,inc,burn)

SCRSELECT documentation built on Aug. 24, 2017, 1:02 a.m.