# Compute Distance or Similarity Matrices Computes a distance or similarity matrix between rows of a data frame or matrix, supporting a wide variety of distance metrics. ```r calculate_distances( x, method = "gower", output_format = "dist", squared = FALSE, p = NULL, similarity_transform = "linear", ... ) ``` ## Arguments - `x`: A matrix or data.frame. Each row represents an observation. - `method`: A string specifying the distance/similarity method. Supported: * Binary : `"jaccard"`, `"dice"`, `"sokal_michener"`, `"russell_rao"`, `"sokal_sneath"`, `"kulczynski"`,`"hamming"`. * Categorical : `"matching_coefficient"`. * Continuous : `"euclidean"`, `"euclidean_standardized"`, `"manhattan"`, `"minkowski"`, `"canberra"`, `"maximum"`, `"cosine"`, `"correlation"`, `"mahalanobis"`. * Mixed : `"gower"`. - `output_format`: Output format: `"dist"` (distance object), `"matrix"` (numeric matrix), or `"similarity"` (only for binary/categorical/mixed methods). - `squared`: Logical; if `TRUE`, returns squared distances (not applied to similarities). - `p`: Numeric; the power parameter for the Minkowski distance (required if `method = "minkowski"`). - `similarity_transform`: Character string; if `output_format = "similarity"`, this specifies the formula to convert distances to similarity scores. Supported: * `"linear"` (default): $s_{ij} = 1 - \delta_{ij}$ * `"sqrt"`: $s_{ij} = 1 - \delta_{ij}^2$ - `...`: Additional arguments passed to underlying functions. ## Returns Depending on `output_format`, returns: * dist object (if `output_format = "dist"`) * numeric matrix (if `output_format = "matrix"` or `"output_format = similarity"`) ## Details When `output_format = "similarity"`, the function transforms computed distances into similarity scores using one of the supported transformations. The similarity transformation options are: - **`"linear"`**: Direct inversion of distance: $s_{ij} = 1 - \delta_{ij}$. - **`"sqrt"`**: Squared distance inversion: $s_{ij} = 1 - \delta_{ij}^2$, which may better preserve Euclidean properties. ## Examples ```r # Load example dataset data("Data_HC_contamination", package = "dbrobust") df <- Data_HC_contamination # --- Quick Example --- numeric_data <- df[1:10, 1:4] # subset for speed d_euclid <- calculate_distances( numeric_data, method = "euclidean", output_format = "matrix" ) # Load example dataset data("Data_HC_contamination", package = "dbrobust") df <- Data_HC_contamination[1:20,] # Example 1: Euclidean distance (numeric variables only) numeric_data <- df[, 1:4] d_euclid <- calculate_distances( numeric_data, method = "euclidean", output_format = "matrix" ) # Example 2: Manhattan distance d_manhattan <- calculate_distances( numeric_data, method = "manhattan", output_format = "matrix" ) # Example 3: Categorical distance using Matching Coefficient categorical_data <- df[, 5:7] d_match <- calculate_distances( categorical_data, method = "matching_coefficient", output_format = "matrix" ) # Example 4: Mixed data distance using Gower (automatic type detection, asymmetric binary) d_gower_asym <- calculate_distances( df, method = "gower", output_format = "dist", binary_asym = TRUE ) # Example 5: Minkowski distance with p = 3 d_minkowski <- calculate_distances( numeric_data, method = "minkowski", p = 3, output_format = "matrix" ) # Example 6: Jaccard distance for binary variables binary_data <- df[, 8:9] d_jaccard <- calculate_distances( binary_data, method = "jaccard", output_format = "matrix" ) # Example 7: Mahalanobis distance d_mahal <- calculate_distances( numeric_data, method = "mahalanobis", output_format = "matrix" ) # Example 8: Manual selection of variables for Gower distance continuous_vars <- 1:4 binary_vars <- 8:9 categorical_vars <- 5:7 d_gower_manual <- calculate_distances( df, method = "gower", output_format = "dist", continuous_cols = continuous_vars, binary_cols = binary_vars, categorical_cols = categorical_vars ) ``` ## See Also `dist` for basic distance measures, `dist.binary` for binary distances, `dist` for advanced metrics like cosine or correlation

calculate_distances function