ising {rIsing} | R Documentation |
Ising Model selection using L1-regularized logistic regression and extended BIC.
ising(X, gamma = 0.5, min_sd = 0, nlambda = 50, lambda.min.ratio = 0.001, symmetrize = "mean")
X |
The design matrix. |
gamma |
(non-negative double) Parameter for the extended BIC (default 0.5). Higher gamma encourages sparsity. See references for more details. |
min_sd |
(non-negative double) Columns of |
nlambda |
(positive integer) The number of parameters in the regularization path (default 50). A longer regularization path will likely yield more accurate results, but will take more time to run. |
lambda.min.ratio |
(non-negative double) The ratio |
symmetrize |
The method used to symmetrize the output adjacency matrix. Must be one of "min", "max", "mean" (default), or FALSE. "min" and "max" correspond to the Wainwright min/max, respectively (see reference 1). "mean" corresponds to the coefficient-wise mean of the output adjacency matrix and its transpose. If FALSE, the output matrix is not symmetrized. |
A list containing the estimated adjacency matrix (Theta
) and the optimal regularization parameter for each node (lambda
), as selected by extended BIC.
Ravikumar, P., Wainwright, M. J. and Lafferty, J. D. (2010). High-dimensional Ising model selection using L1-regularized logistic regression. https://arxiv.org/pdf/1010.0311v1
Barber, R.F., Drton, M. (2015). High-dimensional Ising model selection with Bayesian information criteria. https://arxiv.org/pdf/1403.3374v2
## Not run: # simulate a dataset using IsingSampler library(IsingSampler) n = 1e3 p = 10 Theta <- matrix(sample(c(-0.5,0,0.5), replace = TRUE, size = p*p), nrow = p, ncol = p) Theta <- Theta + t(Theta) # adjacency matrix must be symmetric diag(Theta) <- 0 X <- unname(as.matrix(IsingSampler(n, graph = Theta, thresholds = 0, method = "direct") )) m1 <- ising(X, symmetrize = "mean", gamma = 0.5, nlambda = 50) # Visualize output using igraph library(igraph) ig <- graph_from_adjacency_matrix(m1$Theta, "undirected", weighted = TRUE, diag = FALSE) plot.igraph(ig, vertex.color = "skyblue") ## End(Not run)