ROCSI (version 0.1.0)

MClogit: 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"
)

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

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

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]