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Tune Index Resolution

Source: R/tune_index_res.R
tune_index_res.Rd

Automatically finds the optimal lattice index resolution for your data and query pattern using adaptive bracketing search. Runs test queries with different resolutions and recommends the one with the fastest compute speed.

Usage

tune_index_res(
  x,
  pool,
  downsample = 1,
  seed = NULL,
  select = "all",
  stat = NULL,
  max_clim = NULL,
  max_geog = NULL,
  k = NULL,
  weight = NULL,
  theta = NULL,
  x_cov = NULL,
  values = NULL,
  coord_type = c("auto", "lonlat", "projected"),
  n_threads = NULL,
  default_res = 16L,
  verbose = FALSE
)

Arguments

x

Focal locations for which analogs will be found. Should be a matrix/data.frame with columns x, y, and climate variables, or a SpatRaster with climate variable layers.

pool

The reference dataset to search for analogs. Either:

  • Matrix/data.frame with columns x, y, and climate variables, or SpatRaster with climate variable layers, OR

  • An analog_index object created by build_analog_index() (for repeated queries).

downsample

Optional downsampling rate (0-1) for the reference pool, indicating the proportion of points to retain. Values < 1 reduce memory and improve speed at some cost to precision. Default is 1.0 (no downsampling). Ignored if pool is a pre-built index.

seed

Optional random seed for reproducible downsampling. If NULL (default), uses current R random state. Ignored if pool is a pre-built index or downsample = 1.

select

Character string specifying the analog selection strategy. One of:

  • "all" (default): Select all analogs that satisfy the max_clim and max_geog constraints.

  • "knn_clim": For each focal, select up to k analogs with smallest climate distance, subject to filters.

  • "knn_geog": For each focal, select up to k analogs with smallest geographic distance, subject to filters.

stat

Statistic(s) used to aggregate selected analogs. Either:

  • NULL or "none": Return all selected analog pairs as a data.frame.

  • "count": For each focal, count the number of selected analogs.

  • "sum_weights": For each focal, sum the weights of selected analogs (see weight and theta).

  • "mean_weights": For each focal, mean of weights of selected analogs.

  • "sum": Sum of values across analogs (requires values).

  • "mean": Mean of values across analogs (requires values).

  • "weighted_sum": Sum of (value × weight) across analogs (requires values and weight).

  • "weighted_mean": Weighted mean of values across analogs (requires values and weight).

  • "ess": Kish's effective sample size (ESS), computed as the squared sum of weights divided by the sum of squared weights (requires weight).

  • A character vector combining multiple stats (e.g., c("count", "sum", "mean")). Note: "none" cannot be combined with other stats.

max_clim

Maximum climate distance constraint (default: NULL = no climate constraint). Can be either:

  • A scalar: Euclidean radius in climate space (e.g., 0.5)

  • A vector: Per-variable absolute differences (length must equal number of climate variables)

Only reference locations within this climate distance are considered. When x_cov is provided, scalar thresholds are interpreted in Mahalanobis distance units.

max_geog

Maximum geographic distance constraint (default: NULL = no geographic constraint). When specified, only reference locations within this distance are considered. Radius units should be specified in kilometers if coord_type = "lonlat", or in projected coordinate units if coord_type = "projected".

k

Number of nearest analogs to return per focal location for kNN selection modes. Required when select is "knn_geog" or "knn_clim"; must be NULL for select = "all".

weight

Weighting function for matches, used only when stat includes "sum_weights" or "mean_weights". One of:

  • "uniform": All matches weighted equally (weight = 1.0).

  • "inverse_clim": Inverse climate distance, weight = 1 / (climate_distance + eps), with epsilon given by theta.

  • "inverse_geog": Inverse geographic distance, weight = 1 / (geographic_distance + eps), with epsilon given by theta.

  • "gaussian_clim": Gaussian kernel on climate distance, weight = exp(-climate_distance^2 / (2sigma^2)), with sigma given by theta. "gaussian_geog": Gaussian kernel on geographic distance, weight = exp(-geographic_distance^2 / (2sigma^2)), with sigma given by theta.

  • "gaussian_joint": Gaussian kernel on combined distance, weight = exp(-(clim_dist^2 / (2sigma_clim^2) + geog_dist^2 / (2sigma_geog^2))), with sigmas given by theta.

  • "inverse_joint": Inverse joint distance, weight = 1 / (sqrt(clim_dist^2 + geog_dist^2) + eps), with epsilon given by theta.

theta

Optional numeric parameter used by weighting functions when stat includes "sum_weights" or "mean_weights" and weight is not "uniform". Interpretation depends on weight:

  • For "inverse_clim" or "inverse_geog": epsilon value added to distances (scalar; default: 1e-12 for climate, 1e-6 for geography).

  • For "gaussian_clim" or "gaussian_geog": sigma bandwidth parameter (scalar; larger values = slower decay with distance).

  • For "gaussian_joint" or "inverse_joint": 2-element vector c(theta_clim, theta_geog) (defaults: 1 for climate, 1 for geography).

x_cov

Optional focal-specific covariance matrices for Mahalanobis distance calculations. Should be a matrix or data.frame with one row per focal location and one column per unique covariance component, or a SpatRaster with a layer for each component. For n climate variables, there are n*(n+1)/2 unique components, ordered as: variances first (diagonals), then covariances (upper triangle by row).

values

Optional user-defined variables for each reference location in pool to aggregate across selected analogs. Can be a numeric vector (single variable), matrix or data.frame with numeric columns (multiple variables), or a SpatRaster with one or more numeic layers. Must have exactly the same number of reference locations as pool.

When provided, enables value-based aggregation stats "sum", "mean", "weighted_sum", and "weighted_mean". For stat = NULL/"none" (pairs mode), value columns are included in output for each analog pair.

coord_type

Coordinate system type:

  • "auto" (default): Automatically detect from coordinate ranges.

  • "lonlat": Unprojected lon/lat coordinates (uses great-circle distance; assumes max_geog is in km).

  • "projected": Projected XY coordinates (uses planar distance; assumes max_geog is in projection units).

n_threads

Optional integer number of threads to use for the computation. If NULL (default), the global RcppParallel setting is used (see RcppParallel::setThreadOptions).

default_res

Default resolution to use as starting point for search. Default is 16.

verbose

Logical; if TRUE, print the selected resolution. Default is FALSE.

Value

An integer giving the recommended index resolution (bins per dimension).

Details

The function uses an adaptive bracketing algorithm:

  1. Starts with three resolutions: default/2, default, default*2

  2. Evaluates elapsed time for each

  3. If minimum is at an edge, expands search in that direction

  4. Returns resolution with lowest elapsed time

This typically requires only 3-5 query evaluations total, making it much faster than exhaustive grid search.

The function only performs tuning for non-trivial problem sizes (>2000 focal points). For smaller datasets, it returns the default resolution.

A subsample of focal points is used for benchmarking to keep tuning fast while still being representative of actual query performance.

Examples

if (FALSE) { # \dontrun{
# Find optimal resolution for velocity queries
optimal_res <- tune_index_res(
  x = sample_sites,
  pool = climate_data,
  select = "knn_geog",
  stat = NULL,
  max_clim = 0.5,
  k = 1
)

# Use the optimized resolution
index <- build_analog_index(climate_data, index_res = optimal_res)
} # }