bnlearn (version 0.8)

arc.strength: Measure the strength of the arcs

Description

Strength of the probabilistic relations expressed by one or more arcs of the Bayesian network.

Usage

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

Arguments

x

an object of class bn.

data

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

criterion

the label of either a score or an independence test. See bnlearn-package for details.

...

additional tuning parameters for the network score. See score for details.

debug

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

Value

• arc.strength returns a data frame with three columns: from, to and strength. If criterion is a conditional indipendence test the strength is a p-value (so the lower the value, the stronger the relationship); otherwise it's the score gain/loss which would be caused by the arc's removal.

See Also

choose.direction, score.

Examples

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