ROCSI {ROCSI}

ROCSI

Description

function for ROCSI

Usage

ROCSI(
  Dtrain,
  Dtest = NULL,
  yvar,
  xvars,
  trtvar,
  cvar = NULL,
  nfolds = 5,
  type = "binary"
)

Arguments

Dtrain

data matrix for training dataset

Dtest

optional data matrix for testing dataset

yvar

column name for outcome

xvars

a string vector of column names for input markers

trtvar

column name for treatment (the column should contain binary code with 1 being treatment and 0 being control)

cvar

column name for censor (the column should contain binary code with 1 being event and 0 being censored)

nfolds

n fold CV used for cv.glmnet

type

outcome type ("binary" for binary outcome and "survival" for time-to-event outcome)

Details

function for ROCSI

Value

A list with ROCSI output

beta.aABC

final beta estimated from ROCSI based on ABC^{(acv)}

beta.1se

final beta estimated from lambda.1se based on nfold CV

lambda.aABC

optimal lambda selected by optimizing ABC^{(acv)}

fit.cv

fitted cv.glmnet model

log

log matrix of all lambdas and ABCs

abc.test

ABC in testing dataset based on optimal beta

abc.test1se

ABC in testing dataset based on 1se beta

predScore

a data.frame of testing data and its predictive signature scores (based on beta.aABC) for each subjects

predScore.1se

a data.frame of testing data and its predictive signature scores (based on beta.1se) for each subjects

Examples

n <- 100
k <- 5
prevalence <- sqrt(0.5)
rho<-0.2
sig2 <- 2
rhos.bt.real <- c(0, rep(0.1, (k-3)))*sig2
y.sig2 <- 1
yvar="y.binary"
xvars=paste("x", c(1:k), sep="")
trtvar="treatment"
prog.eff <- 0.5
effect.size <- 1
a.constent <- effect.size/(2*(1-prevalence))
ObsData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
                    sig2=sig2, y.sig2=y.sig2, rho=rho,
                    rhos.bt.real=rhos.bt.real, a.constent=a.constent)
TestData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
                     sig2=sig2, y.sig2=y.sig2, rho=rho,
                     rhos.bt.real=rhos.bt.real, a.constent=a.constent)
bst.aabc <- ROCSI(Dtrain=ObsData$data, Dtest = TestData$data, yvar=yvar,
xvars=xvars, trtvar=trtvar, cvar=NULL, nfolds=5, type="binary")
bst.aabc$beta.aABC
bst.aabc$log
bst.aabc$abc.test
bst.aabc$beta.1se
bst.aabc$abc.test1se