PenalisedGraphical: Graphical LASSO
In sharp: Stability-enHanced Approaches using Resampling Procedures

PenalisedGraphical

R Documentation

Graphical LASSO

Description

Runs the graphical LASSO algorithm for estimation of a Gaussian Graphical Model (GGM). This function is not using stability.

Usage

PenalisedGraphical(
  xdata,
  pk = NULL,
  Lambda,
  Sequential_template = NULL,
  scale = TRUE,
  start = "cold",
  output_omega = FALSE,
  ...
)

Arguments

`xdata`	matrix with observations as rows and variables as columns.
`pk`	optional vector encoding the grouping structure. Only used for multi-block stability selection where `pk` indicates the number of variables in each group. If `pk=NULL`, single-block stability selection is performed.
`Lambda`	matrix of parameters controlling the level of sparsity.
`Sequential_template`	logical matrix encoding the type of procedure to use for data with multiple blocks in stability selection graphical modelling. For multi-block estimation, the stability selection model is constructed as the union of block-specific stable edges estimated while the others are weakly penalised (`TRUE` only for the block currently being calibrated and `FALSE` for other blocks). Other approaches with joint calibration of the blocks are allowed (all entries are set to `TRUE`).
`scale`	logical indicating if the correlation (`scale=TRUE`) or covariance (`scale=FALSE`) matrix should be used as input of `glassoFast` if `implementation=PenalisedGraphical`. Otherwise, this argument must be used in the function provided in `implementation`.
`start`	character string indicating if the algorithm should be initialised at the estimated (inverse) covariance with previous penalty parameters (`start="warm"`) or not (`start="cold"`). Using `start="warm"` can speed-up the computations, but could lead to convergence issues (in particular with small `Lambda_cardinal`). Only used for `implementation=PenalisedGraphical` (see argument `"start"` in `glassoFast`).
`output_omega`	logical indicating if the estimated precision matrices should be stored and returned.
`...`	additional parameters passed to the function provided in `implementation`.

Details

The use of the procedure from Equation (4) or (5) is controlled by the argument "Sequential_template".

Value

An array with binary and symmetric adjacency matrices along the third dimension.

References

\insertRef

GraphicalLassoTibshiranisharp

Examples

# Data simulation
set.seed(1)
simul <- SimulateGraphical()

# Running graphical LASSO
myglasso <- PenalisedGraphical(
  xdata = simul$data,
  Lambda = matrix(c(0.1, 0.2), ncol = 1)
)

# Returning estimated precision matrix
myglasso <- PenalisedGraphical(
  xdata = simul$data,
  Lambda = matrix(c(0.1, 0.2), ncol = 1),
  output_omega = TRUE
)

sharp documentation built on April 11, 2025, 5:44 p.m.