autohd (version 0.1.0)

imphdsurv: High dimensional missing data imputation and survival analysis using survMCmulti with mediation analysis

Description

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

Usage

imphdsurv(m, n, Surv, Event, time, 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.

time

"Column/Variable name" consisting time of repeated observations.

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 with multiple covariates.

Value

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

Details

High dimensional missing data imputation and performing mediation analysis using survMCmulti. It works in a multivariate setup.

Examples

# NOT RUN {
##
# }
# NOT RUN {
imphdsurv(m=11,n=25,Surv="OS",Event="event",time="Visit",ths=0.02,chn=6,i=10,
          adp=100,b=10,d=10,data=srdata)
##
# }