View source: R/scoreagainstdag.R
| scoreagainstDAG | R Documentation | 
This function calculates the score of a given sample against a DAG represented by its incidence matrix.
scoreagainstDAG(
  scorepar,
  incidence,
  datatoscore = NULL,
  marginalise = FALSE,
  onlymain = FALSE,
  bdecatCvec = NULL
)
| scorepar | an object of class  | 
| incidence | a square matrix of dimensions equal to the number of variables with entries in  | 
| datatoscore | (optional) a matrix (vector) containing binary (for BDe score) or continuous (for the BGe score) observations (or just one observation)  to be scored; the number of columns should be equal to the number of variables in the Bayesian network, the number of rows should be equal to the number of observations; by default all data from  | 
| marginalise | (optional for continuous data) defines, whether to use the posterior mean for scoring (default) or to marginalise over the posterior distribution (more computationally costly) | 
| onlymain | (optional), defines the the score is computed for nodes excluding 'bgnodes'; FALSE by default | 
| bdecatCvec | (optional for categorical data) | 
the log of the BDe/BGe score of given observations against a DAG
Jack Kuipers, Polina Suter
Heckerman D and Geiger D, (1995). Learning Bayesian networks: A unification for discrete and Gaussian domains. In Eleventh Conference on Uncertainty in Artificial Intelligence, pages 274-284, 1995.
 Asiascore<-scoreparameters("bde", Asia[1:100,]) #we wish to score only first 100 observations
 scoreagainstDAG(Asiascore, Asiamat) 
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