plot_mds: Plot MDS Results with Grouped Scatter and Density Plots...

In dbrobust: Robust Distance-Based Visualization and Analysis of Mixed-Type Data

Plot MDS Results with Grouped Scatter and Density Plots (Internal)

Description

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.

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.

Value

A ggmatrix object from GGally representing the pairs plot with scatterplots and density plots.

Examples

# 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"
)

dbrobust documentation built on Nov. 5, 2025, 6:24 p.m.