# Plot MDS Results with Grouped Scatter and Density Plots (Internal) This internal function performs classical or weighted Multidimensional Scaling (MDS) on a given distance matrix and visualizes the resulting coordinates using a pairwise scatterplot matrix with density plots on the diagonal. Grouping information can be provided for colored visual separation. ## Arguments - `dist_mat`: A distance matrix or object convertible to a distance matrix. - `k`: Integer. Number of dimensions to retain in MDS (default is 3). - `weights`: Optional numeric vector of weights for weighted MDS. If `NULL`, classical MDS is performed. - `group`: Optional factor or vector indicating group membership for observations, used for coloring plots. - `main_title`: Optional character string for the main plot title. ## Returns A `ggmatrix` object from `GGally` representing the pairs plot with scatterplots and density plots. ## Details This is an internal helper function. It is not recommended to call `plot_mds()` directly. Instead, use `visualize_distances()`, which wraps this function. Weighted MDS is performed with `vegan::wcmdscale` if `weights` are provided; otherwise, classical MDS (`cmdscale`) is used. Diagonal panels show density plots by group, and off-diagonal panels show scatter plots by group. ## Examples ```r # Load example dataset data("Data_HC_contamination", package = "dbrobust") # Subset of 20 rows Data_small <- Data_HC_contamination[1:20, ] # Define variable types cont_vars <- c("V1", "V2", "V3", "V4") cat_vars <- c("V5", "V6", "V7") bin_vars <- c("V8", "V9") # Use column 'w_loop' as weights w <- Data_small$w_loop # Compute robust distances using GGower 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 MDS dbrobust::plot_mds( dist_mat = dist_sq_ggower, k = 2, group = group_factor, main_title = "MDS of Data_HC_contamination (GGower) with Outliers" ) ```

plot_mds function