Nothing
# simulate data with measurement error neither from classical measurement error model nore from
# our own model 1
# nX - number of cases
# nY - number of controls
# X.i.obs = X.i.true+e.X.i
# Y.j.obs = Y.j.true+e.Y.j
#
# log(X.i.true) ~ Normal(lambda, sigma_X^2), i=1, ..., nX
# log(Y.j.true) ~ Normal(lambda-\mu, sigma_Y^2), j=1, ..., nY
#
# e.X.i~N(0, sigma.e.X^2)
# e.Y.i~N(0, sigma.e.Y^2)
genSimDataModelII=function(nX, nY, mu, lambda, sigma.X2, sigma.Y2, sigma.e.X, sigma.e.Y)
{
x.true=exp(rnorm(nX, mean=lambda, sd=sqrt(sigma.X2)))
y.true=exp(rnorm(nY, mean=lambda-mu, sd=sqrt(sigma.Y2)))
e.X=rnorm(nX, mean=0, sd=sigma.e.X)
e.Y=rnorm(nY, mean=0, sd=sigma.e.Y)
x.obs=x.true+e.X
y.obs=y.true+e.Y
#####################
# simulate replicated data
#####################
e.X.rep=rnorm(nX, mean=0, sd=sigma.e.X)
e.Y.rep=rnorm(nY, mean=0, sd=sigma.e.Y)
x.obs.rep=x.true+e.X.rep
y.obs.rep=y.true+e.Y.rep
###################
AUC.true = pnorm(mu/sqrt(sigma.X2+sigma.Y2))
# form data frame with columns
# 'y' -- observations
# 'subjID' -- subject ID
# 'grp' -- group indicator
# 'myrep' -- replication indicator
nSubj=nX+nY
datFrame=data.frame(y=c(x.obs, y.obs, x.obs.rep, y.obs.rep),
subjID=c(seq(from=1, to=nSubj, by=1), seq(from=1, to=nSubj, by=1)),
grp=c(rep(1, nX), rep(0, nY), rep(1, nX), rep(0, nY)),
myrep=c(rep(1, nSubj), rep(2, nSubj)))
res <- list(datFrame=datFrame, AUC.true = AUC.true)
invisible(res)
}
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