AIC/BIC regularization parameter selection

Description

aicAndbicLambdaSelection is a function designed to select the regularization parameter in graphical models. It selects the graph with smallest AIC or BIC coefficients.

Usage

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aicAndbicLambdaSelection(obj,  y, criterion = c("AIC", "BIC"))

Arguments

obj

an object of class huge or camel.tiger.

y

original n \times p data set.

criterion

coefficients and optimal lambdas to be stored: to select from "AIC", "BIC" or both.

Value

An object of class lambdaSelection containing the following components:

opt.lambda

optimal lambdas for AIC and BIC.

crit.coef

coefficients for each lambda given the criterion AIC and BIC.

criterion

with value defined by argument criterion.

Author(s)

Caballe, Adria <a.caballe@sms.ed.ac.uk>, Natalia Bochkina and Claus Mayer.

References

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.

See Also

lambdaSelection for other lambda selection approaches.

Examples

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# 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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