GraphicalAlgo: Graphical model algorithm

Description

Runs the algorithm specified in the argument implementation and returns the estimated adjacency matrix. This function is not using stability.

Usage

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

Value

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

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 in the underlying feature selection algorithm specified in implementation. If Lambda=NULL and implementation=PenalisedGraphical, LambdaGridGraphical is used to define a relevant grid. Lambda can be provided as a vector or a matrix with length(pk) columns.
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.
implementation: function to use for graphical modelling. If implementation=PenalisedGraphical, the algorithm implemented in glassoFast is used for regularised estimation of a conditional independence graph. Alternatively, a user-defined function can be provided.
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).
...: 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".

Examples

Run this code

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

# Running graphical LASSO
myglasso <- GraphicalAlgo(
  xdata = simul$data,
  Lambda = cbind(c(0.1, 0.2))
)