bnlearn (version 0.8)

score: Score of the Bayesian network

Description

Compute the score of the Bayesian network.

Usage

score(x, data, type = NULL, ..., debug = FALSE)
  ## S3 method for class 'bn':
logLik(object, data, ...)
  ## S3 method for class 'bn':
AIC(object, data, ..., k = 1)

Arguments

x

an object of class bn.

object

an object of class bn.

data

a data frame, containing the data the Bayesian network was learned from.

type

a character string, the label of a score. Possible values are lik (multinomial likelihood), loglik (multinomial loglikelihood), aic (Akaike Information Criterion), <

debug

a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

...

extra arguments from the generic method (for the AIC and logLik functions, currently ignored) or additional tuning parameters (for the score function).

k

a numeric value, the penalty per parameter to be used; the default k = 1 gives the expression used to compute the AIC in the context of scoring Bayesian networks.

Value

• A numeric value, the score of the Bayesian network.

Details

Additional parameters of the score function:

• iss: the imaginary sample size, used by the Bayesian Dirichlet equivalent score and the Bayesian Gaussian posterior density. The default value is twice the number of cells of the joint contingency table (for compatibility with thedealpackage) for thebdescore, and twice the number of independent parameters for thebgescore.
• k: the penalty per parameter to be used by the AIC and BIC scores. The default value is1for AIC andlog(nrow(data))/2for BIC.
• phi: the prior phi matrix formula to use in the Bayesian Gaussian equivalent (bge) score. Possible values areheckerman(default) andbottcher(the one used by default in thedealpackage.)

References

D. M. Chickering. A Transformational Characterization of Equivalent Bayesian Network Structures. In Proceedings of 11th Conference on Uncertainty in Artificial Intelligence, pages 87-98. Morgan Kaufmann Publishers Inc., 1995.

D. Heckerman, D. Geiger and D. Chieckering. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data. Microsoft Research Technical Report MSR-TR-94-09.

See Also

choose.direction, arc.strength.

Examples

data(learning.test)
res = set.arc(gs(learning.test), "A", "B")
score(res, learning.test, type = "bde")
# [1] -24002.36
## let's see score equivalence in action!
res2 = set.arc(gs(learning.test), "B", "A")
score(res2, learning.test, type = "bde")
# [1] -24002.36

## k2 score on the other hand is not score equivalent.
score(res, learning.test, type = "k2")
# [1] -23958.70
score(res2, learning.test, type = "k2")
# [1] -23957.68

## equivalent to logLik(res, learning.test)
score(res, learning.test, type = "loglik")
# [1] -23832.13

## equivalent to AIC(res, learning.test)
score(res, learning.test, type = "aic")
# [1] -23873.13