arc.strength: Measure the strength of the arcs

Description

Strength of the probabilistic relations expressed by the arcs of the Bayesian network.

Usage

arc.strength(x, data, criterion = NULL, ..., debug = FALSE)

Arguments

an object of class bn.

data

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

criterion

a character string, the label of a score function, the label of an independence test or bootstrap. See bnlearn-package for details on the first two possibilities.

...

additional tuning parameters for the network score (if criterion is the label of a score function, see score for details), the conditional independence test (currently the only

debug

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

Value

arc.strength returns an object of class bn.strength. See bn.strength class for details.

Details

If criterion is a conditional independence test the strength is a p-value (so the lower the value, the stronger the relationship). The only possible additional parameter is B, the number of permutations to be generated for a permutation test.

If criterion is a score function it's the score gain/loss which would be caused by the arc's removal. There may be additional parameters depending on the choice of the score, see score for details.

If criterion is bootstrap the strength is computed as in boot.strength. The additional parameters are R, m, algorithm and algorithm.args; if the latter two are not specified, the values stored in x are used.

Examples

Run this code

data(learning.test)
res = gs(learning.test)
res = set.arc(res, "A", "B")
arc.strength(res, learning.test)
#   from to      strength
# 1    A  B  0.000000e+00
# 2    A  D  0.000000e+00
# 3    B  E 1.024198e-320
# 4    C  D  0.000000e+00
# 5    F  E 3.935648e-245
arc.strength(res, learning.test, criterion = "aic")
#   from to   strength
# 1    A  B -1166.9139
# 2    A  D -1978.0531
# 3    B  E  -746.8954
# 4    C  D  -862.8637
# 5    F  E  -568.7816
res = set.arc(res, "B", "A")
# A -> B and B -> A have the same strength because they
# are score equivalent.
arc.strength(res, learning.test, criterion = "aic")
#   from to   strength
# 1    A  D -1978.0531
# 2    B  E  -746.8954
# 3    C  D  -862.8637
# 4    F  E  -568.7816
# 5    B  A -1166.9139
arc.strength(res, data = learning.test, criterion = "bootstrap",
  R = 200, algorithm.args = list(alpha = 0.10))
#   from to strength
# 1    A  B        1
# 2    A  D        1
# 3    B  E        1
# 4    C  D        1
# 5    F  E        1