hdsurvma: High dimensional survival analysis using SurvMCmulti with...

In autohd: High Dimensional Bayesian Survival Mediation Analysis

Description

Given the dimension of variables and survival information the function filters significant variables, allowing the user to perform survival analysis with high number of iterations. Further, it performs mediation analysis among the signifiant variables and provides handful variables with their alpha.a values which are mediator model exposure coefficients and beta.a coefficients.

Usage

hdsurvma(m, n, Surv, Event, ths, chn, i, adp, b, d, data)

Arguments

m	Starting column number from where high dimensional variates to be selected.
n	Ending column number till where high dimensional variates to be selected.
Surv	"Column/Variable name" consisting duration of survival.
Event	"Column/Variable name" consisting survival event.
ths	A numeric between 0 to 100.
chn	Number of MCMC chains to perform survival analysis.
i	Number of MCMC iterations to perform survival analysis.
adp	Number of MCMC adaptations to perform survival analysis.
b	Number of MCMC iterations to burn.
d	Number of draws.
data	High dimensional data containing survival observations and high dimensional covariates.

Value

Data frame containing the beta and alpha values of active variables among the significant variables.

Examples

## Not run: 
data2 <- hnscc2[1:50,]
hdsurvma(m=8,n=15,Surv="os",Event="death",ths=0.02,chn=4,i=10,adp=100,b=10,d=10,data=data2)

## End(Not run)

autohd documentation built on May 10, 2021, 9:10 a.m.