View source: R/penalised_models.R
PenalisedGraphical | R Documentation |
Runs the graphical LASSO algorithm for estimation of a Gaussian Graphical Model (GGM). This function is not using stability.
PenalisedGraphical(
xdata,
pk = NULL,
Lambda,
Sequential_template = NULL,
scale = TRUE,
start = "cold",
output_omega = FALSE,
...
)
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. |
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 ( |
start |
character string indicating if the algorithm should be
initialised at the estimated (inverse) covariance with previous penalty
parameters ( |
output_omega |
logical indicating if the estimated precision matrices should be stored and returned. |
... |
additional parameters passed to the function provided in
|
The use of the procedure from Equation (4) or (5) is controlled by the argument "Sequential_template".
An array with binary and symmetric adjacency matrices along the third dimension.
GraphicalLassoTibshiranisharp
GraphicalModel
Other underlying algorithm functions:
CART()
,
ClusteringAlgo()
,
PenalisedOpenMx()
,
PenalisedRegression()
# 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
)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.