GraphicalAlgo {sharp} | R Documentation |
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",
...
)
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 |
Lambda |
matrix of parameters controlling the level of sparsity in the
underlying feature selection algorithm specified in |
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 ( |
scale |
logical indicating if the correlation ( |
implementation |
function to use for graphical modelling. If
|
start |
character string indicating if the algorithm should be
initialised at the estimated (inverse) covariance with previous penalty
parameters ( |
... |
additional parameters passed to the function provided in
|
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.
See Also
GraphicalModel
, PenalisedGraphical
Other wrapping functions:
SelectionAlgo()
Examples
# Data simulation
set.seed(1)
simul <- SimulateGraphical()
# Running graphical LASSO
myglasso <- GraphicalAlgo(
xdata = simul$data,
Lambda = cbind(c(0.1, 0.2))
)