\HeaderA{getpvalue}{Function to obtain P values from the Edge permutation and Node
permutation tests respectively}{getpvalue}
\keyword{htest}{getpvalue}
\begin{Description}\relax
The function takes as inputs two adjacency matrices. Let X denote the observed number of 
edges in common between the two adjacency matrices.  To
test the significance of the correlation between the two data sources,
the function performs N random edge permutations and random node
permutations respectively. For each permutation test, the function
outputs the proportion of N realizations that resulted in X edges or more at the intersection of the two datasources
\end{Description}
\begin{Usage}
\begin{verbatim}getpvalue(act.mat, nonact.mat, num.iterations = 1000)\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{act.mat}] Adjacency matrix corresponding to first data
source. That is, the i,j element of this matrix is 1 if data source
one specifies a functional link between genes i and j 
\item[\code{nonact.mat}] Adjacency matrix corresponding to first data
source. That is, the i,j element of this matrix is 1 if data source
two specifies a functional link between genes i and j
\item[\code{num.iterations}] Number of realizations from random edge (node)
permutation to be obtained
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
We note that the first adjacency matrix, denoted act.mat is
the data source that is permutated with respect to edges or notes
\end{Details}
\begin{Value}
A vector of length 2, where the first element is the P value
from Random Edge Permutation and the second element is the P value
from Random Node Permutation
\end{Value}
\begin{Author}\relax
Raji Balasubramanian \email{rbalasub@hsph.harvard.edu}
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{permEdgesM2M}{permEdgesM2M}}, \code{\LinkA{permNodesM2M}{permNodesM2M}}, \code{\LinkA{makeClustM}{makeClustM}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
act.mat <- matrix(0,3,3)
act.mat[2,1] <- 1 
act.mat[3,1] <- 1
nonact.mat  <- matrix(0,3,3)
nonact.mat[2,1] <- 1
nonact.mat[3,2] <- 1
p.val <- getpvalue(act.mat, nonact.mat, num.iterations = 100)
print(p.val)
\end{ExampleCode}
\end{Examples}

