dispersionIndicators (version 0.1.5)

compute_icm_distances: Compute ICM (Integrated Covariance Mahalanobis) Distances

Description

This function computes Mahalanobis distances in PCA-reduced space, with options for individual, intra-group, and inter-group comparisons. It supports batch-wise analysis and shrinkage covariance estimation for robustness.

Usage

compute_icm_distances(
  data,
  batch_col = NULL,
  mode = c("individual", "intra", "inter", "all"),
  variance_threshold = 0.95,
  center_method_individual = c("global", "batch"),
  center_method_inter = c("mean", "median"),
  ref_batch = NULL
)

Value

A list containing data.frames of computed distances depending on the selected mode(s).

Arguments

data

A data.frame containing numeric variables and optionally a batch/group column.

batch_col

Name of the column representing batch or group (optional).

mode

Mode of computation: "individual", "intra", "inter", or "all".

variance_threshold

Threshold for cumulative variance to retain in PCA (default: 0.95).

center_method_individual

Method for centering in "individual" mode: "global" or "batch" (default: "global").

center_method_inter

Method for centering in "inter" mode: "mean" or "median" (default: "mean").

ref_batch

Reference batch name to compute inter-batch distances (default: first batch).

Examples

data <- data.frame(matrix(rnorm(100*5), ncol = 5))
data$Batch <- rep(c("A", "B", "C", "D"), each = 25)
result <- compute_icm_distances(
 data,
 batch_col = "Batch",
 mode = "all",
 center_method_individual = "batch",
 center_method_inter = "mean"
)
print(result)