clusterGGM (version 0.1.1)

get_clusters: Extract the Cluster Assignment

Description

Extract a cluster assignment obtained via the clusterpath estimator of the Gaussian graphical model (CGGM).

Usage

get_clusters(object, ...)
# S3 method for CGGM
get_clusters(object, index, ...)
# S3 method for CGGM_refit
get_clusters(object, index, ...)
# S3 method for CGGM_CV
get_clusters(object, which = NULL, ...)

Value

An integer vector giving the obtained cluster assignment for each variable.

For the "CGGM_CV" method (see cggm_cv()), the returned cluster assignment corresponds to the optimal values of the tuning parameters.

Arguments

object

an object from which to extract the cluster assignment.

...

additional arguments are currently ignored.

index

an integer specifying the step along the clusterpath for which to extract the cluster assignment.

which

a character string specifying for which solution to extract the cluster assignment. Possible values are "refit" for the solution including the refitting step (see cggm_refit()), or "fit" for the solution without without the refitting step (see cggm()). If NULL (the default), the solution with the better cross-validation score is used.

Author

Daniel J.W. Touw

References

D.J.W. Touw, A. Alfons, P.J.F. Groenen and I. Wilms (2025) Clusterpath Gaussian Graphical Modeling. arXiv:2407.00644. doi:10.48550/arXiv.2407.00644.

See Also

cggm(), cggm_refit(), cggm_cv()

get_Theta()

Examples

# Generate data
set.seed(3)
Theta <- matrix(
  c(2, 1, 0, 0,
    1, 2, 0, 0,
    0, 0, 4, 1,
    0, 0, 1, 4),
  nrow = 4
)
X <- mvtnorm::rmvnorm(n = 100, sigma = solve(Theta))

# Estimate the covariance matrix
S <- cov(X)

# Compute the weight matrix for the clusterpath (clustering) weights
W_cpath <- clusterpath_weights(S, phi = 1, k = 2)

# Compute the weight matrix for the lasso (sparsity) weights
W_lasso <- lasso_weights(S)

# Set values to be used for the aggregation parameter
lambdas <- seq(0, 0.2, by = 0.01)

# Estimate the precision matrix while automatically expanding
# the sequence of values for the aggregation parameter
fit <- cggm(S, W_cpath = W_cpath, lambda_cpath = lambdas,
            W_lasso = W_lasso, lambda_lasso = 0.2,
            expand = TRUE)

# Cluster membership for 2 clusters
get_clusters(fit, index = fit$cluster_solution_index[2])


# Apply the refitting step to the results, estimating the
# precision matrix based on the clustering and sparsity
# patterns but without additional shrinkage
refit <- cggm_refit(fit)

# Cluster membership for 2 clusters
get_clusters(refit, index = refit$cluster_solution_index[2])