Description Usage Arguments Value See Also Examples
This is a wrapper for the whole inference procedure and makes use of
innovateGeneUntilSaturated
.
For every gene it infers regulatory functions until the upper bound is
reached or there is no more improvement from adding more functions.
1 2 | inferPBN(ts.multi, p = 0.01, L.max = 2, n.cores = detectCores() - 1,
seed = 111, partial = FALSE, verbal = FALSE)
|
ts.multi |
A list of timeseries. |
p |
The noise probability to use for the network. |
L.max |
The maximum number of regulatory networks to try to infer. |
n.cores |
The number of cores to be used. |
seed |
The random seed to use. |
partial |
Whether or not to *also* infer a network where only partial optimization over the last threshold parameter is performed. Defaults to FALSE. |
verbal |
Show progress report? Defaults to FALSE. |
A list containing the inferred network and the time taken to perform the inference.
1 2 3 4 5 | net.true <- createNetwork(inputProbabilities=c(0.5, 0.5), n=5, k=2)
ts.multi <- simulateNetwork(net.true, 10, 20)
inferred.list <- inferPBN(ts.multi, n.cores=1)
net.inferred <- inferred.list$net.inferred
time <- inferred.list$time.complete
|
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