simData(nTrain=100,
nGr1=floor(nTrain/2),
nBiom=50,nRep=3,
sdW=1.0,
sdB=1.0,rho=0,
sigma=0.1,diffExpr=TRUE,
foldMin=2,
orderBiom=TRUE,
baseExpr=NULL)nGr1) and group 2.floor(nTrain/2).rho are restricted between 0 and 0.95 inclusive.nBiom to be used as base
expressions $\mu$. See realBiomarker for details.nTrain by nBiom+1. The first
column is a factor (class) representing the group memberships of
the samples.(-1,1) to
characterise up- or down-regulation.Assuming that $Y ~is~ N(\mu, \sigma^2)$, and $A=[a_1,a_2]$, a subset of
$-Inf $$f(y, \mu, \sigma)= (1/\sigma) \phi((y-\mu)/\sigma) / (\Phi((a2-\mu)/\sigma) -
\Phi((a_1-\mu)/\sigma))$$ for $a_1 <= 0="" y="" <="a_2$," and="" otherwise,<="" p=""> where $\mu$ is the mean of the original Normal distribution before truncation,
$\sigma$ is the corresponding standard deviation,$a_2$ is the upper truncation point,
$a_1$ is the lower truncation point, $\phi(x)$ is the density of the
standard normal distribution, and $\Phi(x)$ is the distribution function
of the standard normal distribution. For simData function, we
consider $a_1=log_2(\code{foldMin})$ and $a_2=Inf$. This ensures that the
biomarkers are differentially expressed by a fold change of
foldMin or more.
classificationError