Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/pcLambdaSelection.r
pcLambdaSelection
is a function designed to select the regularization
parameter in graphical models. It selects the graph which captures the biggest drop
in graph connectivity.
1 | pcLambdaSelection(obj)
|
obj |
an object of class |
Path Connectivity (PC) algorithm finds λ by maximizing the biggest drop of
connectivity in estimated graphs. We define connectivity by the average geodesic distance between pairs of nodes
(see graphDist
).
PC gives a fast and suitable way to select λ when there are distinct clusters in the data. Given two graphs, corresponding to two consecutive λ's, the difference between the average geodesic distance will be large if the first graph contains edges that connect different clusters which are not present in the second graph.
Note that PC should be used when fitting graphical models with an equidistant sequence for λ.
An object of class lambdaSelection
containing the following components:
opt.lambda |
optimal lambda. |
crit.coef |
coefficients for each lambda given the criterion PC. |
criterion |
with value |
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.
lambdaSelection
for other lambda selection approaches.
1 2 3 4 5 6 7 8 9 10 | # example to use pc function
EX1 <- pcorSimulator(nobs = 70, nclusters = 3, nnodesxcluster = c(40,30,20),
pattern = "powerLaw")
y <- EX1$y
Lambda.SEQ <- seq(.25,0.70,length.out = 40)
out3 <- huge(y, method = "mb", lambda = Lambda.SEQ)
PC.COEF <- pcLambdaSelection(out3)
print(PC.COEF)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.