# strength of the arcs present in x.
arc.strength(x, data, criterion = NULL, ..., debug = FALSE)
# strength of all possible arcs, as learned from bootstrapped data.
boot.strength(data, cluster = NULL, R = 200, m = nrow(data),
algorithm, algorithm.args = list(), cpdag = TRUE, debug = FALSE)
# strength of all possible arcs, from a list of custom networks.
custom.strength(networks, nodes, weights = NULL, cpdag = TRUE, debug = FALSE)
# average arc strengths.
# S3 method for bn.strength
mean(x, ..., weights = NULL)# averaged network structure.
averaged.network(strength, nodes, threshold)
bn.strength (for mean) or of
class bn (for all other functions).bn or arc
sets (matrices or data frames with two columns, optionally labeled "from"
and "to").parallel integration for details and a simple example.bn.strength, see below.threshold attribute of the strength argument.averaged.network, it defaults to the set of the
unique node labels in the strength argument.bnlearn-package for details.mean) or network structures (in
custom.strength) to compute strength coefficients. If NULL,
weights are assumed to be uniform.TRUE the (PDAG of) the equivalence
class is used instead of the network structure itself. It should make it
easier to identify score-equivalent arcs.gs, iamb,
fast.iamb, inter.iamb, mmpc, hc, tabu,
mmhc and rsmax2. See bnlearn-package and the
documentation of each algorithm for details.arc.strength, the 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 one is B, the number of permutations). In
mean, additional objects of class bn.strength to average.TRUE a lot of debugging output is
printed; otherwise the function is completely silent.arc.strength, boot.strength, custom.strength and
mean return an object of class bn.strength; boot.strength
and custom.strength also include information about the relative
probabilities of arc directions. averaged.network returns an object of class bn. See bn.strength class and bn-class for details.arc.strength computes a measure of confidence or strength for each
arc, while keeping fixed the rest of the network structure. If criterion is a conditional independence test, the strength is a
p-value (so the lower the value, the stronger the relationship). The
conditional independence test would be that to drop the arc from the
network. The only possible additional argument is B, the number
of permutations to be generated for each permutation test. If criterion is the label of a score function, the strength is
measured by the score gain/loss which would be caused by the arc's removal.
In other words, it is the difference between the score of the network
including the arc and the score of the network in which the arc is not
present. Negative values correspond to decreases in the network score and
positive values correspond to increases in the network score (the stronger
the relationship, the more negative the difference). There may be additional
aguments depending on the choice of the score, see score for
details. boot.strength estimates the strength of each arc as its empirical
frequency over a set of networks learned from bootstrap samples. It computes
the probability of each arc (modulo its direction) and the probabilities of
each arc's directions conditional on the arc being present in the graph (in
either direction). custom.strength takes a list of networks and estimates arc strength
in the same way as boot.strength. Model averaging is supported for objects of class bn.strength returned
by boot.strength or by custom.strength. The
returned network contains the arcs whose strength is greater than the
threshold attribute of the bn.strength object passed to
averaged.network.strength.plot, choose.direction,
score, ci.test.data(learning.test)
res = gs(learning.test)
res = set.arc(res, "A", "B")
arc.strength(res, learning.test)
## Not run: ------------------------------------
# arcs = boot.strength(learning.test, algorithm = "hc")
# arcs[(arcs$strength > 0.85) & (arcs$direction >= 0.5), ]
# averaged.network(arcs)
#
# start = random.graph(nodes = names(learning.test), num = 50)
# netlist = lapply(start, function(net) {
# hc(learning.test, score = "bde", iss = 10, start = net) })
# arcs = custom.strength(netlist, nodes = names(learning.test),
# cpdag = FALSE)
# arcs[(arcs$strength > 0.85) & (arcs$direction >= 0.5), ]
# modelstring(averaged.network(arcs))
## ---------------------------------------------
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