ROCSI (version 0.1.0)

ROCSI: ROCSI

Description

function for ROCSI

Usage

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

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

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

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