auto_select_method: Automatic Resampling Method Selection

Description

Inspects data structure and recommends an appropriate resampling method.

Usage

auto_select_method(data, cluster = NULL)

Value

A list with two character string elements:

method: recommended resampling method name. Values: "clustered_boot" (if cluster provided), "block_boot" (if time series detected), "perm_maxT" (if multivariate matrix detected), "nonparametric_boot" (default for IID univariate data)
rationale: human-readable explanation of recommendation. Describes why chosen method is appropriate and what structure the data exhibits.

Arguments

data: numeric vector, matrix, or time series object representing data to analyze. For vectors: univariate sample. For matrices/data.frames: multivariate data with rows=observations, columns=variables. For ts objects: automatically detected as time series.
cluster: optional vector of cluster IDs (integer or factor). Length must equal nrow(data) for matrices or length(data) for vectors. Identifies hierarchical grouping (e.g., repeated measures within subjects, multiple samples from same source). If NULL (default), clustering is not considered. If provided, triggers clustered bootstrap recommendation.

Details

Decision logic: - If clusters provided: use clustered bootstrap - If time series detected: use block or stationary bootstrap - If multivariate matrix: use permutation maxT - Otherwise (IID data): use standard nonparametric bootstrap

Examples

Run this code

# IID data
x <- rnorm(50)
auto_select_method(x)

# Time series
ts_data <- arima.sim(n = 100, list(ar = 0.7))
auto_select_method(ts_data)

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