Description Usage Arguments Details Examples
These methods convert graph objects (e.g. edgeList)
and objects containing graph data (e.g. sparsebnFit,
sparsebnPath) to other formats including igraph,
graphNEL, network, and
bn-class.
Only graph objects are modified with these methods. For example, if the input
is either sparsebnFit or sparsebnPath,
the output will still be a sparsebnFit or sparsebnPath
object. Only the edges slots will be converted to a different graph type.
This will be the case for the default output from estimate.dag,
so that metadata from the learning phase is not lost during conversion.
If, on the other hand, the input is already an edgeList, then the
output will directly be a graph object.
1 2 3 4 5 6 7 | to_bn(x)
to_graphNEL(x)
to_igraph(x)
to_network(x)
|
x |
An object of type |
to_igraph converts sparsebn objects to igraph-compatible
objects.
to_graph converts sparsebn objects to graphNEL-compatible
objects.
to_network converts sparsebn objects to network-compatible
objects.
to_bn converts sparsebn objects to bn-class-compatible
objects.
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 | ## Not run:
### Learn the cytometry network
library(sparsebn)
data(cytometryContinuous)
cyto.data <- sparsebnData(cytometryContinuous[["data"]],
type = "continuous",
ivn = cytometryContinuous[["ivn"]])
cyto.learn <- estimate.dag(data = cyto.data)
### The output is a sparsebnPath object, which is a list of sparsebnFit objects
class(cyto.learn)
class(cyto.learn[[1]])
### Convert to igraph
cyto.igraph <- to_igraph(cyto.learn)
class(cyto.igraph) # not an igraph object!
class(cyto.igraph[[1]]$edges) # the graph data in the 'edges' slot is converted to igraph
gr <- cyto.igraph[[1]]$edges
### Different behaviour when input is already an edgeList
edgeL <- cyto.learn[[1]]$edges
gr <- to_igraph(edgeL) # input is edgeList, not sparsebnFit or sparsebnPath
class(gr) # igraph object
## End(Not run)
|
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