\HeaderA{idProteinErrorType}{A function to identify those proteins affected by either
stochastic or systematic errors}{idProteinErrorType}
\aliasA{idStochastic}{idProteinErrorType}{idStochastic}
\aliasA{idSystematic}{idProteinErrorType}{idSystematic}
\keyword{datagen}{idProteinErrorType}
\begin{Description}\relax
This function takes in either a bait to prey Graph (matrix) and,
based on a binomial error model, partitions proteins identified as
either affected by systematic or stochastic error.
\end{Description}
\begin{Usage}
\begin{verbatim}
idSystematic(bpMat, viable, bpGraph = FALSE, pThresh = 0.01, pLevels = 1e-4, prob=0.5)
idStochastic(bpMat, bpGraph = FALSE, pThresh = 0.01, pLevels = 1e-4, prob=0.5)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{bpMat}] Either a bait to prey directed graphNEL or its
corresponding adjacency matrix.
\item[\code{viable}] This is a character vector of viable proteins. It is
only used in the idSystematic function.
\item[\code{bpGraph}] A logical. If TRUE, than bpMat is passed in by the user
as a graphNEL.
\item[\code{pThresh}] The p-value threshold for which to partition stochastic
or systematic errors
\item[\code{pLevels}] A numeric vector. It gives the levels to calculate the
countours of the function in p in the (n-in, n-out)-plane
\item[\code{prob}] A numeric. The bias of the coin in the call to pbinom.
\end{ldescription}
\end{Arguments}
\begin{Value}
A character vector of proteins either affected by systematic or
stochastic errors.
\end{Value}
\begin{Author}\relax
T Chiang
\end{Author}
\begin{References}\relax
~put references to the literature/web site here ~
\end{References}
\begin{Examples}
\begin{ExampleCode}
library(ppiData)
idSystematic(Ito2001BPGraph, viableBaits[[1]], bpGraph=TRUE)
\end{ExampleCode}
\end{Examples}

