It results in about g*(d/g)*(d/g-1)*prob/2 edges in the graph. Each pair of off-diagonal elements are set theta=theta=1 for i!=j with the probability probif both i and j belong to the same group, and 0 other wise. (III) "cluster":The row/columns are evenly partitioned into g disjoint groups. Each pair of off-diagonal elements are set theta=theta=1 for i!=j if j also belongs to the same group as i and 0 otherwise. Each group is associated with a "center" row i in that group. (II) "hub":The row/columns are evenly partitioned into g disjoint groups. It results in about d*(d-1)*prob/2 edges in the graph. (I) "random": Each pair of off-diagonal elements are randomly set theta=theta=1 for i!=j with probability prob, and 0 other wise. Given the adjacency matrix theta, the graph patterns are generated as below: If verbose = FALSE, tracing information printing is disabled. Visualize the adjacency matrix of the true graph structure, the graph pattern, the covariance matrix and the empirical covariance matrix. NOT applicable to "hub" or "band" graphs. The default value is about d/20 if d >= 40 and 2 if d 30. The default value is 0.1.įor "cluster" or "hub" graph, g is the number of hubs or clusters in the graph. The default value is 0.3.Ī positive number being added to the diagonal elements of the precision matrix, to control the magnitude of partial correlations. The off-diagonal elements of the precision matrix, controlling the magnitude of partial correlations with u. The graph structure with 4 options: "random", "hub", "cluster", "band" and "scale-free". The number of observations (sample size).
Huge.generator ( n = 200, d = 50, graph = "random", v = NULL, u = NULL, g = NULL, prob = NULL, vis = FALSE, verbose = TRUE ) stockdata: Stock price of S&P 500 companies from 2003 to 2008.print.sim: Print function for S3 class "sim".lect: Print function for S3 class "select".print.roc: Print function for S3 class "roc".print.huge: Print function for S3 class "huge".plot.sim: Plot function for S3 class "sim".lect: Plot function for S3 class "select".plot.roc: Plot function for S3 class "roc".plot.huge: Plot function for S3 class "huge".huge.tiger: Tuning-insensitive graph estimation.lect: Model selection for high-dimensional undirected graph.huge.roc: Draw ROC Curve for a graph path.huge-package: High-Dimensional Undirected Graph Estimation.huge.npn: Nonparanormal(npn) transformation.huge.mb: Meinshausen & Buhlmann graph estimation.huge.glasso: The graphical lasso (glasso) using sparse matrix output.huge.ct: Graph estimation via correlation thresholding (ct).huge: High-dimensional undirected graph estimation.
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