BiDAG (version 2.0.2)

scorespace: Prints 'scorespace' object

Description

Prints 'scorespace' object

Summary of object of class 'scorespace'

Usage

scorespace(
  scorepar,
  alpha = 0.05,
  hardlimit = 14,
  plus1 = TRUE,
  cpdag = TRUE,
  startspace = NULL,
  blacklist = NULL,
  verbose = FALSE
)
# S3 method for scorespace
print(x, ...)
# S3 method for scorespace
summary(object, ...)

Arguments

scorepar

an object of class scoreparameters, containing the data and score scorepareters, see constructor function scoreparameters

alpha

numerical significance value in {0,1} for the conditional independence tests at the PC algorithm stage (by default \(0.4\) for \(n<50\), \(20/n\) for \(n>50\))

hardlimit

integer, limit on the size of parent sets in the search space; by default 14 when MAP=TRUE and 20 when MAP=FALSE

plus1

logical, if TRUE (default) the search is performed on the extended search space

cpdag

logical, if TRUE the CPDAG returned by the PC algorithm will be used as the search space, if FALSE (default) the full undirected skeleton will be used as the search space

startspace

(optional) a square matrix, of dimensions equal to the number of nodes, which defines the search space for the order MCMC in the form of an adjacency matrix. If NULL, the skeleton obtained from the PC-algorithm will be used. If startspace[i,j] equals to 1 (0) it means that the edge from node i to node j is included (excluded) from the search space. To include an edge in both directions, both startspace[i,j] and startspace[j,i] should be 1.

blacklist

(optional) a square matrix, of dimensions equal to the number of nodes, which defines edges to exclude from the search space. If blacklist[i,j] equals to 1 it means that the edge from node i to node j is excluded from the search space.

verbose

logical, if TRUE messages about the algorithm's progress will be printed, FALSE by default

x

object of class 'scorespace'

...

ignored

object

object of class 'scorespace'

Value

Object of class scorespace, a list of three objects: 'adjacency' matrix representiong the search space, 'blacklist' used to exclude edges from the search space and 'tables' containing score quantities for each node needed to run MCMC schemes

References

Friedman N and Koller D (2003). A Bayesian approach to structure discovery in bayesian networks. Machine Learning 50, 95-125.

Examples

# NOT RUN {
#' #find a MAP DAG with search space defined by PC and plus1 neighbourhood
Bostonscore<-scoreparameters("bge",Boston)
Bostonspace<-scorespace(Bostonscore, 0.05, 14)
# }
# NOT RUN {
orderfit<-orderMCMC(Bostonscore, scoretable=Bostonspace)
partitionfit<-orderMCMC(Bostonscore, scoretable=Bostonspace)
# }