rags2ridges: fullMontyS – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

fullMontyS

Wrapper function

Description

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.

Usage

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.

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.

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.

Note

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

Author(s)

Carel F.W. Peeters <cf.peeters@vumc.nl>, Wessel N. van Wieringen

Examples

## 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)

rags2ridges

Ridge Estimation of Precision Matrices from High-Dimensional Data

v2.2.4

GPL (>= 2)

Authors

Carel F.W. Peeters [cre, aut], Anders Ellern Bilgrau [aut], Wessel N. van Wieringen [aut]

Initial release