Description Usage Arguments Details Value Note Author(s) References See Also Examples
View source: R/lambdaSelection.r
lambdaSelection
is a function designed to select the sparsity regularization
parameter in graphical models.
There are available seven different criterion to select lambda with risk functions
based on network characteristics: Path connectivity (PC), AGlommerative NESted (AGNES), Augmented-MSE (A-MSE),
Vulnerability (VUL), AIC/BIC and StARS (from huge
package).
1 2 | lambdaSelection(obj, criterion = c("PC","AGNES",
"A-MSE","VUL","STARS", "AIC", "BIC", "eBIC"), ...)
|
obj |
an object of class |
criterion |
regularization parameter selection approach: name that uniquely identifies |
... |
arguments passed to or from other methods to the low level. See |
This function considers seven ways of selecting the regularization parameter in graphical models by minimizing a certain risk function based only on network characteristics of the underlying structure of Ω
\hatλ = \arg \min_{λ} R(λ, \hat{G}_λ),
where \hat{G}_λ is the estimated graph structure of \hat{Ω}. For
instance see pcLambdaSelection
,
agnesLambdaSelection
, amseLambdaSelection
, vulLambdaSelection
,
aicAndbicLambdaSelection
and huge
for the implemented criterions to select λ.
For wfgl
objects, only criterion = c("PC","AGNES","VUL")
are available.
An object of class lambdaSelection
containing the following components:
opt.lambda |
optimal lambda. |
crit.coef |
coefficients for each lambda given the criterion. |
criterion |
criterion used to select lambda. |
For large dimensions, "A-MSE"
, "VUL"
(the most) and "StARS"
can be computationally intensive.
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.
pcLambdaSelection
, agnesLambdaSelection
, amseLambdaSelection
vulLambdaSelection
, aicAndbicLambdaSelection
and huge
.
1 2 3 4 5 6 7 8 9 | # example to use agnes 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 = "mb", lambda = Lambda.SEQ)
PC.COEF <- lambdaSelection(out3, criterion = "PC")
#AG.COEF <- lambdaSelection(out3, criterion = "AGNES")
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.