robust_distances: Compute Robust Squared Distances for Mixed Data

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

Compute Robust Squared Distances for Mixed Data

Description

Computes a weighted, robust squared distance matrix for datasets containing continuous, binary, and categorical variables. Continuous variables are handled via a robust Mahalanobis distance, and binary and categorical variables are transformed via similarity coefficients. The output is suitable for Euclidean correction with make_euclidean.

Usage

robust_distances(
  data = NULL,
  cont_vars = NULL,
  bin_vars = NULL,
  cat_vars = NULL,
  w = NULL,
  p = NULL,
  method = c("ggower", "relms"),
  robust_cov = NULL,
  alpha = 0.1,
  return_dist = FALSE
)

Arguments

data	Data frame or numeric matrix containing the observations.
cont_vars	Character vector of column names for continuous variables.
bin_vars	Character vector of column names for binary variables.
cat_vars	Character vector of column names for categorical variables.
w	Numeric vector of observation weights. If NULL, uniform weights are used.
p	Integer vector of length 3: c(#cont, #binary, #categorical). Overrides variable type selection if provided.
method	Character string: either "ggower" or "relms" for distance computation.
robust_cov	Optional. Precomputed robust covariance matrix for continuous variables. If NULL, it will be estimated internally using the specified trimming proportion alpha.
alpha	Numeric trimming proportion for robust covariance of continuous variables.
return_dist	Logical. If TRUE, returns an object of class dist; otherwise, returns a squared distance matrix.

Value

A numeric matrix of squared robust distances (n x n) or a dist object if return_dist = TRUE.

Examples

# Example: Robust Squared Distances for Mixed Data

# Load example data and subset
data("Data_HC_contamination", package = "dbrobust")
Data_small <- Data_HC_contamination[1:50, ]

# Define variable types
cont_vars <- c("V1", "V2", "V3", "V4")  # continuous
cat_vars  <- c("V5", "V6", "V7")        # categorical
bin_vars  <- c("V8", "V9")              # binary

# Use column w_loop as weights
w <- Data_small$w_loop

# -------------------------------
# Method 1: Gower distances
# -------------------------------
dist_sq_ggower <- robust_distances(
  data = Data_small,
  cont_vars = cont_vars,
  bin_vars  = bin_vars,
  cat_vars  = cat_vars,
  w = w,
  alpha = 0.10,
  method = "ggower"
)

# Apply Euclidean correction if needed
res_ggower <- make_euclidean(dist_sq_ggower, w)

# Show first 5x5 block of original and corrected distances
cat("GGower original squared distances (5x5 block):\n")
print(round(dist_sq_ggower[1:5, 1:5], 4))
cat("\nGGower corrected squared distances (5x5 block):\n")
print(round(res_ggower$D_euc[1:5, 1:5], 4))

# -------------------------------
# Method 2: RelMS distances
# -------------------------------
dist_sq_relms <- robust_distances(
  data = Data_small,
  cont_vars = cont_vars,
  bin_vars  = bin_vars,
  cat_vars  = cat_vars,
  w = w,
  alpha = 0.10,
  method = "relms"
)

# Apply Euclidean correction if needed
res_relms <- make_euclidean(dist_sq_relms, w)

# Show first 5x5 block of original and corrected distances
cat("RelMS original squared distances (5x5 block):\n")
print(round(dist_sq_relms[1:5, 1:5], 4))
cat("\nRelMS corrected squared distances (5x5 block):\n")
print(round(res_relms$D_euc[1:5, 1:5], 4))

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