# Plot a Network Graph from a Distance Matrix This internal function visualizes a network graph representation of a distance matrix, where nodes represent observations and edges represent similarity. Groups can be specified for node coloring. A maximum number of nodes can be set to avoid overcrowding, and weak edges are thresholded. ```r plot_qgraph( dist_mat, group = NULL, max_nodes = 100, label_size = 2, edge_threshold = 0.1, layout = "spring", seed = 123, main_title = NULL ) ``` ## Arguments - `dist_mat`: A square distance matrix or a `dist` object. Distances are automatically normalized to [0,1] and converted to similarity via `1 - distance`. - `group`: Optional factor or vector indicating group membership for nodes, used for coloring. - `max_nodes`: Integer. Maximum number of nodes to plot. If the number of observations exceeds this, stratified sampling is performed to reduce the node count. - `label_size`: Numeric. Size of the node labels. - `edge_threshold`: Numeric between 0 and 1. Edges with similarity below this threshold are removed. - `layout`: Character string specifying the layout algorithm for `qgraph`. Default is `"spring"`. - `seed`: Integer. Random seed used for reproducibility during sampling and layout. - `main_title`: Optional character string specifying the main title of the plot. ## Returns Invisibly returns `NULL`. The plot is drawn as a side effect. ## Details This function is internal and not intended for direct use. It is called by `visualize_distances()` to display network graphs of robust distances. Features: * Converts `dist` objects to matrices automatically. * Downsamples nodes if the number of observations exceeds `max_nodes`, using stratified sampling by group. * Normalizes the distance matrix to [0,1] and converts it to similarity (1 - distance). * Removes weak edges below `edge_threshold`. * Colors nodes according to group membership. * Adds a main title using `title()` after plotting with `qgraph`. ## Examples ```r # -------------------------------------- # Network Graph Example from Robust Distances # -------------------------------------- data("Data_HC_contamination", package = "dbrobust") # Subset small dataset Data_small <- Data_HC_contamination[1:20, ] cont_vars <- c("V1", "V2", "V3", "V4") cat_vars <- c("V5", "V6", "V7") bin_vars <- c("V8", "V9") w <- Data_small$w_loop # Compute GGower robust distances dist_sq_ggower <- dbrobust::robust_distances( data = Data_small, cont_vars = cont_vars, bin_vars = bin_vars, cat_vars = cat_vars, w = w, alpha = 0.10, method = "ggower" ) # Create factor indicating Normal vs Outlier n_obs <- nrow(dist_sq_ggower) group_vec <- rep("Normal", n_obs) group_vec[attr(dist_sq_ggower, "outlier_idx")] <- "Outlier" group_factor <- factor(group_vec, levels = c("Normal", "Outlier")) # Plot network graph (small, for CRAN) dbrobust::plot_qgraph( dist_mat = sqrt(dist_sq_ggower), group = group_factor, max_nodes = 10, label_size = 2, edge_threshold = 0.1, layout = "spring", seed = 123, main_title = "GGower Network Graph with Outliers" ) ```

plot_qgraph function