Description Usage Arguments Value Author(s) References See Also Examples
aicAndbicLambdaSelection
is a function designed to select the regularization
parameter in graphical models. It selects the graph with smallest AIC or BIC coefficients.
1 | aicAndbicLambdaSelection(obj, y, criterion = c("AIC", "BIC", "eBIC"), phi=1)
|
obj |
an object of class |
y |
original n \times p data set. |
criterion |
coefficients and optimal lambdas to be stored: to select from |
phi |
weight used in the eBIC criterion (see reference). |
An object of class lambdaSelection
containing the following components:
opt.lambda |
optimal lambdas for AIC, BIC and eBIC. |
crit.coef |
coefficients for each lambda given the criterion AIC, BIC and eBIC. |
criterion |
with value defined by argument |
Caballe, Adria <a.caballe@sms.ed.ac.uk>, Natalia Bochkina and Claus Mayer.
Caballe, A., N. Bochkina, and C. Mayer (2016). Selection of the Regularization Parameter in Graphical Models using network charactaristics. eprint arXiv:1509.05326, 1-25.
Chen, J. and Z. Chen (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika 95(3), 759-771.
lambdaSelection
for other lambda selection approaches.
1 2 3 4 5 6 7 8 9 10 11 | # example to use aicAndBic function
EX1 <- pcorSimulator(nobs = 50, nclusters = 3, nnodesxcluster = c(40,30,30),
pattern = "powerLaw")
y <- EX1$y
Lambda.SEQ <- seq(.35, 0.70, length.out = 40)
out3 <- huge(y, method = "glasso", lambda = Lambda.SEQ, cov.output = TRUE)
AIC.COEF <- aicAndbicLambdaSelection(out3, y = y)
print(AIC.COEF)
|
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