Wrapper function

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
)

Arguments

Y: Data matrix. Variables assumed to be represented by columns.
lambdaMin: A numeric giving the minimum value for the penalty parameter.
lambdaMax: A numeric giving the maximum value for the penalty parameter.
target: A target matrix (in precision terms) for Type I ridge estimators.
dir: A character specifying the directory in which the (visual) output is to be stored.
fileTypeFig: A character determining the file type of visual output. Must be one of: "pdf", "eps".
FDRcut: A numeric indicating the cut-off for partial correlation element selection based on local FDR thresholding.
nOutput: A logical indicating if numeric output should be returned.
verbose: A logical indicating if progress updates should be printed on screen.

Value

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 numeric giving the optimal value of the penalty parameter.
optPrec: A matrix representing the regularized precision matrix under the optimal value of the penalty parameter.
sparseParCor: A matrix representing the sparsified partial correlation matrix.
networkStats: A matrix giving the calculated network statistics.

Details

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.

Note

We consider this to be a preliminary version of an envisioned wrapper than will take better form with subsequent versions of rags2ridges.

Author

Carel F.W. Peeters <carel.peeters@wur.nl>, Wessel N. van Wieringen

Examples


if (FALSE) { # \dontrun{
## 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)} # }