# Create Graphs from cglasso or cggm Objects ‘to_graph’ returns a named list of graphs using the results of an object of class ‘cglasso’ or ‘cggm’ . ```r to_graph(object, GoF = AIC, lambda.id, rho.id, weighted = FALSE, simplify = TRUE, ...) ``` ## Arguments - `object`: an object inheriting class ‘cglasso’ , that is, the output of the model-fitting functions `cglasso` and `cggm`. - `GoF`: a valid goodness-of-fit function, such as `AIC.cglasso` or `BIC.cglasso`, or an object of class ‘GoF’ . - `lambda.id`: optional. If `object` has class ‘cglasso’ , this argument is an integer used to identify a specific fitted cglasso model, otherwise (i.e. if the `object` has class ‘cggm’ ) this argument can be omitted. See section Details for more details. - `rho.id`: optional. If `object` has class ‘cglasso’ , this argument is an integer used to identify a specific fitted cglasso model, otherwise (i.e. if `object` has class ‘cggm’ ) this argument can be omitted. See section Details for more details. - `weighted`: logical. Should weighted graphs be created? Default is `FALSE`. - `simplify`: logical. Should isolated vertices be removed from the graph? Default is `TRUE`, i.e., isolated vertices are removed. - ``: further arguments passed to the chosen goodness-of-fit function (argument ‘GoF’ ). ## Details ‘to_graph’ returns a named list of graphs using the results of an object of class ‘cglasso’ or ‘cggm’ . If `object` has class ‘cglasso’ , then the goodness-of-fit function passed through the argument `GoF` is used to identify the adjacency matrix (`object$InfoStructure$Adj_yy`) describing the undirected edges among the $p$ response variables. If the model is fitted using $q$ predictors, then the matrix describing the effects of the predictors onto the response variables (see object$InfoStructure$Adj_xy) is also returned. Finally, these matrices are used to return an undirected and directed graph. Opionally, the user can identify a specific fitted model using the arguments `lambda.id` and `rho.id`. If `object` has class ‘cggm’ , then `GoF`, `lambda.id` and `rho.id` can be omitted. If argument weighted is set equal to ‘TRUE’ , then the estimated precision matrix and, if available, the estimated regression coefficient matrix are used to return weighted graphs. In this case, edges associated with positive estimates are shown using a solid line. Otherwise, a dashed line is used. ## Returns ‘to_graph’ returns an object of S3 class ‘cglasso2igraph’ , i.e., a named list containing the following components: - **Gyy**: an undirected graph representing the conditional dependence structure among the $p$ response variables. - **Gxy**: a directed graph representing the effetcs of the $q$ predictors onto the $p$ response variables. Each component is an object of class `igraph`. ## Author(s) Luigi Augugliaro (luigi.augugliaro@unipa.it ) ## See Also `cglasso`, `cggm` and `plot.cglasso2igraph`. For more details about the object of class ‘igraph’ , the interested reader is referred to the package `igraph`. ## Examples ```r set.seed(123) # Y ~ N(0, Sigma) and probability of left/right censored values equal to 0.05 n <- 100L p <- 3L rho <- 0.3 Sigma <- outer(1L:p, 1L:p, function(i, j) rho^abs(i - j)) Z <- rcggm(n = n, Sigma = Sigma, probl = 0.05, probr = 0.05) out <- cglasso(. ~ ., data = Z) out.graph <- to_graph(out) out.graph # Y ~ N(b0 + XB, Sigma) and probability of left/right censored values equal to 0.05 n <- 100L p <- 3L q <- 2L b0 <- runif(p) B <- matrix(runif(q * p), nrow = q, ncol = p) X <- matrix(rnorm(n * q), nrow = n, ncol = q) rho <- 0.3 Sigma <- outer(1L:p, 1L:p, function(i, j) rho^abs(i - j)) Z <- rcggm(n = n, b0 = b0, X = X, B = B, Sigma = Sigma, probl = 0.05, probr = 0.05) out <- cglasso(. ~ ., data = Z) out.graph <- to_graph(out, lambda.id = 3, rho.id = 3, weighted = TRUE) out.graph ```

to_graph function