Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/agnesLambdaSelection.r
agnesLambdaSelection
is a function designed to select the regularization
parameter in graphical models. It selects the most clustered conditional dependence graph structure where clusters are
defined by the hierarchical algorithm agnes
(See details).
1 2 3 4 | agnesLambdaSelection(obj, way = "direct", nite = 10, subsvec = NULL,
eps = 0.05, until = NULL, minNodes = 30,
distF = c("correlation","shortPath"))
|
obj |
an object of class |
way |
name that uniquely identifies |
nite |
vector with the number of iterations used for each lambda (only if |
subsvec |
vector with the number of subsamples used for each lambda (only if |
eps |
acceptance tolerance for subsets of variables. |
until |
the last path used in |
minNodes |
minimum number of nodes with connections to compute the AGNES coefficient
(the coefficient is zero for paths with less nodes than |
distF |
distance function used to find the dissimilarity matrix from the graph: name that uniquely identifies
|
AGNES algorithm finds λ by minimizing the risk function
R_{AGNES}(λ) = -AC(λ)
where AC(λ) is the AGNES coefficient calculated using the R function
agnes
. Using AGNES we select the λ that maximizes the between
vs within cluster dissimilarities ratio given the dissimilarity matrix of the graph
(see graphCorr
and graphDist
for possible dissimilarities).
A variable subset selection algorithm is available to estimate AC(λ) for
very high-dimensional data. It is recommended in order to save memory space and computational time.
Especially way = "int.sampling"
which tends to finds similar lambda selections to the default
procedure.
agnesLambdaSelection
gives a good recovery of global network characteristics when the true partial correlation matrix is block diagonal.
An object of class lambdaSelection
containing the following components:
opt.lambda |
optimal lambda. |
crit.coef |
coefficients for each lambda given the criterion AGNES. |
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 and agnes
for clustering implementation.
1 2 3 4 5 6 7 8 9 10 | # example to use agnes 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)
AG.COEF <- agnesLambdaSelection(out3, distF = "shortPath", way = "direct")
print(AG.COEF)
|
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