MClogit {ROCSI}

MClogit

Description

function for modified covariate methods based on glmnet

Usage

MClogit(
  dataset,
  yvar,
  xvars,
  trtvar,
  cvar = NULL,
  nfolds = 5,
  type = "binary",
  newx = NULL,
  bestsub = "lambda.1se",
  type.measure = "auc"
)

Arguments

dataset

data matrix for training 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)

newx

data matrix for testing dataset X

bestsub

criteria for best lambda, used by glmnet

type.measure

type of measure used by glmnet

Details

function for ROCSI

Value

A list with ROCSI output

x.logit

final beta estimated from MClogit

predScore

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

abc

ABC in testing dataset based on optimal beta

fit.cv

the fitted glmnet object

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.mod <- MClogit(dataset=ObsData$data, yvar=yvar, xvars=xvars,
trtvar=trtvar, nfolds = 5, newx=TestData$data,
type="binary", bestsub="lambda.1se")
bst.mod$abc
bst.mod$x.logit[-1,1]