Biology Reference
In-Depth Information
44 PIP3 PIP2 1.00 0.5
56 p44.42 pakts473 1.00 0.5
66 pakts473 p44.42 1.00 0.5
67 pakts473 PKA 1.00 0.5
77 PKA pakts473 1.00 0.5
89 PKC P38 1.00 0.5
90 PKC pjnk 1.00 0.5
99 P38 PKC 1.00 0.5
109 pjnk PKC 1.00 0.5
> avg.catnet = averaged.network(sa, threshold = 0.85)
Again, avg.catnet presents some small differences from both avg.boot
and avg.start . Such differences can be attributed to the different scores and
structure learning algorithms used to build the sets of high-scoring networks. In
particular, it is very common for arc directions to change between different learning
algorithms as a result of score equivalence.
2.5.2 Choosing the Significance Threshold
The value of the threshold beyond which an arc is considered significant, which is
often called the significance threshold , does not seem to have a huge influence on
the analysis of the data analyzed in Sachs et al. ( 2005 ). In fact, any value between
0
.
5and0
85 yields exactly the same results. So, for instance,
> all.equal(averaged.network(boot, threshold = 0.50),
+
.
averaged.network(boot, threshold = 0.70))
[1] TRUE
The same holds for avg.catnet and avg.start . However, this is often not
the case. Therefore, it is important to use a statistically motivated algorithm for
choosing a suitable threshold instead of relying on ad hoc values.
A solution to this problem is presented in Scutari and Nagarajan ( 2012 )and
implemented in bnlearn as the default value for the threshold argument in
averaged.network .
> averaged.network(boot)
Random/Generated Bayesian network
model:
[praf][plcg][p44.42][PKC][pmek|praf][PIP2|plcg]
[pakts473|p44.42][P38|PKC][PIP3|plcg:PIP2]
[PKA|p44.42:pakts473][pjnk|PKC:P38]
nodes:
11
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