estimate.dag: Estimate a DAG from data
In sparsebn: Learning Sparse Bayesian Networks from High-Dimensional Data

Description Usage Arguments Details Value Examples

View source: R/sparsebn-main.R

Estimate the structure of a DAG (Bayesian network) from data. Works with any combination of discrete / continuous and observational / experimental data.

estimate.dag(
  data,
  lambdas = NULL,
  lambdas.length = 20,
  whitelist = NULL,
  blacklist = NULL,
  error.tol = 1e-04,
  max.iters = NULL,
  edge.threshold = NULL,
  concavity = 2,
  weight.scale = 1,
  convLb = 0.01,
  upperbound = 100,
  adaptive = FALSE,
  verbose = FALSE
)

`data`	Data as `sparsebnData`.
`lambdas`	(optional) Numeric vector containing a grid of lambda values (i.e. regularization parameters) to use in the solution path. If missing, a default grid of values will be used based on a decreasing log-scale (see also generate.lambdas).
`lambdas.length`	Integer number of values to include in the solution path. If `lambdas` has also been specified, this value will be ignored.
`whitelist`	A two-column matrix of edges that are guaranteed to be in each estimate (a "white list"). Each row in this matrix corresponds to an edge that is to be whitelisted. These edges can be specified by node name (as a `character` matrix), or by index (as a `numeric` matrix).
`blacklist`	A two-column matrix of edges that are guaranteed to be absent from each estimate (a "black list"). See argument "`whitelist`" above for more details.
`error.tol`	Error tolerance for the algorithm, used to test for convergence.
`max.iters`	Maximum number of iterations for each internal sweep.
`edge.threshold`	Threshold parameter used to terminate the algorithm whenever the number of edges in the current estimate has `> edge.threshold` edges. NOTE: This is not the same as `alpha` in `ccdr.run`.
`concavity`	(CCDr only) Value of concavity parameter. If `gamma > 0`, then the MCP will be used with `gamma` as the concavity parameter. If `gamma < 0`, then the L1 penalty will be used and this value is otherwise ignored.
`weight.scale`	(CD only) A postitive number to scale weight matrix.
`convLb`	(CD only) Small positive number used in Hessian approximation.
`upperbound`	(CD only) A large positive value used to truncate the adaptive weights. A -1 value indicates that there is no truncation.
`adaptive`	(CD only) `TRUE / FALSE`, if `TRUE` the adaptive algorithm will be run.
`verbose`	`TRUE / FALSE` whether or not to print out progress and summary reports.

For details on the underlying methods, see ccdr.run and cd.run.

A sparsebnPath object.

# Estimate a DAG from the cytometry data
data(cytometryContinuous)
dat <- sparsebnData(cytometryContinuous$data, type = "c", ivn = cytometryContinuous$ivn)
estimate.dag(dat)