fullMontyS | R Documentation |
Function that forms a wrapper around certain rags2ridges
functionalities. More specifically, it (automatically) invokes
functionalities to get from high-dimensional data to a penalized precision
estimate, to the corresponding conditional independence graph and topology
summaries.
fullMontyS(
Y,
lambdaMin,
lambdaMax,
target = default.target(covML(Y)),
dir = getwd(),
fileTypeFig = "pdf",
FDRcut = 0.9,
nOutput = TRUE,
verbose = TRUE
)
Y |
Data |
lambdaMin |
A |
lambdaMax |
A |
target |
A target |
dir |
A |
fileTypeFig |
A |
FDRcut |
A |
nOutput |
A |
verbose |
A |
The wrapper always uses the alternative ridge precision estimator (see
ridgeP
) with target
as the target matrix. The optimal
value for the penalty parameter is determined by employing Brent's method to
the calculation of a cross-validated negative log-likelihood score (see
optPenalty.LOOCVauto
). The support of the regularized
precision matrix is determined by way of local FDR thresholding (see
sparsify
). The corresponding conditional independence graph is
visualized using Ugraph
with type = "fancy"
. This
visualization as well as the calculation of network statistics (see
GGMnetworkStats
) is based on the standardization of the
regularized and sparsified precision matrix to a partial correlation matrix.
The function stores in the specified directory dir
a
condition number plot (either .pdf or .eps file), a visualization of the
network (either .pdf or .eps file), and a file containing network statistics
(.txt file). When nOutput = TRUE
the function also returns an object
of class list
:
optLambda |
A |
optPrec |
A |
sparseParCor |
A |
networkStats |
A |
We consider this to be a preliminary version of an envisioned wrapper
than will take better form with subsequent versions of rags2ridges
.
Carel F.W. Peeters <carel.peeters@wur.nl>, Wessel N. van Wieringen
ridgeP
, conditionNumberPlot
,
optPenalty.LOOCVauto
, sparsify
,
Ugraph
, GGMnetworkStats
## Not run:
## Obtain some (high-dimensional) data
p = 25
n = 10
set.seed(333)
X = matrix(rnorm(n*p), nrow = n, ncol = p)
colnames(X)[1:25] = letters[1:25]
## Employ the wrapper function
theWorks <- fullMontyS(X, lambdaMin = .5, lambdaMax = 30)
## End(Not run)
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