Computes the quantities to be used for assessment from a proposed and a set of reference edges. For cases where there is incomplete information about the true underlying network.
1  contabs.prelim(network, reference, known = NULL)

network 
A network represented as a data frame in which each row corresponds to a directed edge (regulatorgene pair) with nonzero estimated probability. The first column gives the name of the regulator, the second column gives the name of the regulated gene, and the third column gives the estimated probability for the regulatorgene pair. If the third column is omitted it is assumed that all edges are assigned probability 1. 
reference 
A reference network represented as a twocolumn data frame in which each row corresponds to a regulatorgene pair (or network edge), and the columns give the corresponding regulator and target gene names, respectively. This reference network is used as the standard for determining contingency table entries. 
known 
A 2column matrix of regulatory relationships that were known (hardcoded)
in the modeling process that produced 
This function accomplishes the preprocessing step from
contabs.newtBMA
to produce the appropriate input to
contabs
to produce contingency tables.
A list with the following components:
network 
A network represented as a data frame in which each row corresponds to
an edge with nonzero estimated probability. This is the input

reference 
A reference network represented as a twocolumn data frame in which each row corresponds to an edge, and the columns give the regulator and target genes, respectively. This is the input reference reduced by the known regulatory relationships. 
size 
The size of the network formed by pairs (r,g) in which r is any regulator
g is any gene from the output version of 
networkBMA,contabs.netwBMA,contabs
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  ## Not run:
data(vignette)
dim(timeSeries)
colnames(timeSeries)
table(timeSeries$replicate)
table(timeSeries$time)
dim(reg.known)
colnames(reg.known)
dim(reg.prob)
edges < networkBMA(data = timeSeries[,(1:2)],
nTimePoints = length(unique(timeSeries$time)),
prior.prob = reg.prob, known = reg.known)
prelim < contabs.prelim( network = edges, reference = referenceNetwork,
known = reg.known)
contabs( network = prelim$network, reference = prelim$reference,
size = prelim$size, thresholds = prelim$thresholds)
contabs.netwBMA( network = edges, reference = referenceNetwork,
known = reg.known)
## End(Not run)

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