Description Usage Arguments Details Value Author(s) References See Also Examples
vulLambdaSelection
is a function designed to select the regularization
parameter in graphical models. It selects the graph with largest average nodes vulnerability.
1 | vulLambdaSelection(obj, loo = FALSE, subOut = 10, nite = 50)
|
obj |
an object of class |
loo |
if |
subOut |
number of variables left out in each iteration (only used if |
nite |
number of iterations (only used if |
Vulnerability algorithm finds lambda by minimizing the risk function
R_{VUL}(λ) = - ∑_{i=1}^p \frac{E^λ-E_i^λ}{E^λ}
where E^λ is the global efficiency of the original network and
E^λ_i is the global efficiency of the network once eliminating the node
i. Global efficiency is defined by the harmonic mean of the geodesic distance
(see graphDist
).
Vulnerability gives λ selection that contains the most vulnerable graph, meaning that the removal of a node in the network in average would affect the most the estimated graph.
An object of class lambdaSelection
containing the following components:
opt.lambda |
optimal lambda. |
crit.coef |
coefficients for each lambda given the criterion VUL. |
criterion |
with value |
Caballe, Adria <a.caballe@sms.ed.ac.uk>, Natalia Bochkina and Claus Mayer.
Costa, L. and F. Rodrigues (2007). Characterization of complex networks: A survey of measurements. Advances in Physics 56(1), 167-242.
lambdaSelection
for other lambda selection approaches.
1 2 3 4 5 6 7 8 9 10 11 12 | # example to use vul function
EX1 <- pcorSimulator(nobs = 50, nclusters = 2, nnodesxcluster = c(40,30),
pattern="powerLaw")
y <- EX1$y
Lambda.SEQ <- seq(.35, 0.70, length.out = 10)
out3 <- huge(y, method = "mb", lambda = Lambda.SEQ)
## not run
#VUL.COEF <- vulLambdaSelection(out3)
#print(VUL.COEF)
|
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