sparsebnPath: sparsebnPath class
In itsrainingdata/sparsebnUtils: Utilities for Learning Sparse Bayesian Networks

Description Usage Arguments Details Methods Examples

Convenience wrapper class for solution paths of DAG learning algorithms: This class represents an entire solution path of an algorithm. Its components are of type sparsebnFit. Also inherits from list.

sparsebnPath(x)

is.sparsebnPath(x)

## S3 method for class 'sparsebnPath'
print(x, verbose = FALSE, ...)

## S3 method for class 'sparsebnPath'
summary(object, ...)

## S3 method for class 'sparsebnPath'
plot(x, labels = FALSE, ...)

`x`	A `list` or an object of type `sparsebnPath`. Should only be used internally.
`verbose`	If `TRUE`, then each estimate in the solution path is printed separately. Do not use for large graphs or large solution paths. (default = `FALSE`)
`...`	(optional) additional arguments.
`object`	an object of type `sparsebnPath`
`labels`	`TRUE` or `FALSE`. Whether or not to print out labels with summary information for each plot in the solution path.

Each value of lambda in the (discrete) solution path corresponds to a single DAG estimate (see Aragam and Zhou (2015) for details). Internally, this estimate is represented by a sparsebnFit object. The full solution path is then represented as a list of sparsebnFit objects: This class is essentially a wrapper for this list.

Most methods for sparsebnPath objects simply apply lapply to the object in question. The exceptions to this rule apply when the output will always be the same for every component; e.g. num.nodes and num.samples.

get.adjacency.matrix, get.lambdas, num.nodes, num.edges, num.samples

## 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)
print(cyto.learn)
plot(cyto.learn)

## End(Not run)