Description Usage Arguments Details Slots Methods Examples
Main class for representing DAG estimates. Represents a single DAG estimate in a solution path.
1 2 3 4 5 6 7 8 9 10 11 12 | sparsebnFit(x)
is.sparsebnFit(x)
## S3 method for class 'sparsebnFit'
print(x, maxsize = 20, ...)
## S3 method for class 'sparsebnFit'
summary(object, ...)
## S3 method for class 'sparsebnFit'
plot(x, ...)
|
x |
A |
maxsize |
If the number of nodes in a graph is ≤ |
... |
(optional) additional arguments. |
object |
an object of type |
This is the main class for storing and manipulating the output of estimate.dag
.
The main slot of interest is edges
, which stores the graph as an edgeList
object. If desired, this slot can be changed to hold a graphNEL
,
igraph
, or network
object if desired (see
setGraphPackage
). For anything beyond simply inspecting the graph, it is recommended
to use one of these packages.
Since edgeList
s do not contain information on the node names, the second slot
nodes
stores this information. The indices in edges
are in one-to-one
correspondence with the names in the nodes
vector. The lambda
slot stores
the regularization parameter used to estimate the graph.
Other slots include nedge
, for the number of edges; pp
, for p = number of nodes;
nn
, for n = number of samples, and time
, for the time in seconds needed to
estimate this graph. Note that these slots are mainly for internal use, and in particular
it is best to query the number of nodes via num.nodes
, the number of edges
via num.edges
, and the number of samples via num.samples
.
By default, only small graphs are printed, but this behaviour can be overridden via the
maxsize
argument to print
. To view a list of parents for a specific subset of
nodes, use show.parents
.
Generally speaking, it should not be necessary to construct a sparsebnFit
object
manually. Furthermore, these estimates should always be wrapped up in a sparsebnPath
object, but can be handled separately if desired (be careful!).
edges
(edgeList
) Edge list of estimated DAG (see edgeList
).
nodes
(character
) Vector of node names.
lambda
(numeric
) Value of lambda for this estimate.
nedge
(integer
) Number of edges in this estimate.
pp
(integer
) Number of nodes.
nn
(integer
) Number of observations this estimate was based on.
time
(numeric
) Time in seconds to generate this estimate.
get.adjacency.matrix
,
num.nodes
,
num.edges
,
num.samples
,
show.parents
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:
### Learn the cytometry network
library(sparsebn)
data(cytometryContinuous) # from the sparsebn package
cyto.data <- sparsebnData(cytometryContinuous[["data"]], type = "continuous")
cyto.learn <- estimate.dag(cyto.data)
### Inspect the output
class(cyto.learn[[1]])
print(cyto.learn[[2]])
show.parents(cyto.learn[[1]], c("raf", "mek", "plc"))
### Manipulate a particular graph
cyto.fit <- cyto.learn[[7]]
num.nodes(cyto.fit)
num.edges(cyto.fit)
show.parents(cyto.fit, c("raf", "mek", "plc"))
plot(cyto.fit)
### Use graph package instead of edgeLists
setGraphPackage("graph", coerce = TRUE) # set sparsebn to use graph package
cyto.edges <- cyto.fit$edges
degree(cyto.edges) # only available with graph package
isConnected(cyto.edges) # only available with graph package
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.