PenalisedOpenMx: Penalised Structural Equation Model

In sharp: Stability-enHanced Approaches using Resampling Procedures

Penalised Structural Equation Model

Description

Runs penalised Structural Equation Modelling using implementations from OpenMx functions (for PenalisedOpenMx), or using series of penalised regressions with glmnet (for PenalisedLinearSystem). The function PenalisedLinearSystem does not accommodate latent variables. These functions are not using stability.

Usage

PenalisedOpenMx(
  xdata,
  adjacency,
  penalised = NULL,
  residual_covariance = NULL,
  Lambda,
  ...
)

PenalisedLinearSystem(xdata, adjacency, penalised = NULL, Lambda = NULL, ...)

Arguments

xdata	matrix with observations as rows and variables as columns. Column names must be defined and in line with the row and column names of adjacency.
adjacency	binary adjacency matrix of the Directed Acyclic Graph (transpose of the asymmetric matrix A in Reticular Action Model notation). The row and column names of this matrix must be defined.
penalised	optional binary matrix indicating which coefficients are regularised.
residual_covariance	binary and symmetric matrix encoding the nonzero entries in the residual covariance matrix (symmetric matrix S in Reticular Action Model notation). By default, this is the identity matrix (no residual covariance).
Lambda	matrix of parameters controlling the level of sparsity. Only the minimum, maximum and length are used in PenalisedOpenMx.
...	additional parameters passed to OpenMx functions (for PenalisedOpenMx), or glmnet (for PenalisedLinearSystem).

Value

A list with:

selected	matrix of binary selection status. Rows correspond to different regularisation parameters. Columns correspond to different parameters to estimated.
beta_full	matrix of model coefficients. Rows correspond to different regularisation parameters. Columns correspond to different parameters to estimated.

References

\insertRef

RegSEMsharp

See Also

SelectionAlgo, VariableSelection, OpenMxMatrix, LinearSystemMatrix

Other underlying algorithm functions: CART(), ClusteringAlgo(), PenalisedGraphical(), PenalisedRegression()

Examples

# Data simulation
pk <- c(3, 2, 3)
dag <- LayeredDAG(layers = pk)
theta <- dag
theta[2, 4] <- 0
set.seed(1)
simul <- SimulateStructural(theta = theta, pk = pk, output_matrices = TRUE)

# Running regularised SEM (OpenMx)
if (requireNamespace("OpenMx", quietly = TRUE)) {
  mysem <- PenalisedOpenMx(
    xdata = simul$data, adjacency = dag,
    Lambda = seq(1, 10, 1)
  )
  OpenMxMatrix(vect = mysem$selected[3, ], adjacency = dag)
}

# Running regularised SEM (glmnet)
mysem <- PenalisedLinearSystem(
  xdata = simul$data, adjacency = dag
)
LinearSystemMatrix(vect = mysem$selected[20, ], adjacency = dag)

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