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Analyze pair-wise spatial neighbor patterns

Source: R/synch_neighbor_analysis.R
synch_neighbor_analysis.Rd

Analyzes how long each pair of animals spend at neighboring bins (spatial proximity). Uses bin layout configuration to determine which bins are spatial neighbors. For each pair of animals, calculates the number of bouts they spent as neighbors, total time as neighbors, and average duration per bout.

Usage

synch_neighbor_analysis(
  matrix_data,
  bin_layout = bin_layout2(),
  type = c("feed", "drink", "feed_and_drink"),
  resolution = c("sec", "min"),
  id_col = id_col2()
)

Arguments

matrix_data

Output from matrix_process(), either a single day's matrices or a list of matrices for multiple days. Must contain synch_master_bin2 component with Time column and animal ID columns (values are bin numbers).

bin_layout

Character string representing spatial layout of bins, with rows separated by \n and bins within rows separated by "-". Example: 1-2-3\n4-5-6 represents first row has bin 1, 2, 3, second row has bin 4, 5, 6. Default uses bin_layout2().

type

Character, one of "feed", "drink", "feed_and_drink" to specify the type of activity being analyzed

resolution

Character, either "sec" (default) or "min" for time resolution. Affects bout detection threshold and time calculations.

id_col

Animal ID column name (default current global value from id_col2())

Value

List with three elements, each containing animal × animal matrices (one per day if multi-day input, or single matrix if single day input). Only upper triangle of matrices is filled:

  • bout: Number of distinct bouts each pair spent as neighbors

  • total_time: Total time (seconds or minutes) each pair spent as neighbors

  • avg_duration: Average duration per bout for each pair

Matrix rows and columns are labeled with animal IDs. Pairs that were never neighbors have value 0.

Examples

# Create toy data with animals at different bins
toy_data <- data.frame(
  animal = c(1, 2, 3, 1, 2),
  start = lubridate::ymd_hms(c(
    "2023-01-01 10:00:00", "2023-01-01 10:00:01",
    "2023-01-01 10:00:05", "2023-01-01 10:00:10",
    "2023-01-01 10:00:11"
  )),
  end = lubridate::ymd_hms(c(
    "2023-01-01 10:00:03", "2023-01-01 10:00:04",
    "2023-01-01 10:00:08", "2023-01-01 10:00:13",
    "2023-01-01 10:00:14"
  )),
  bin = c(1, 2, 3, 1, 2),
  start_weight = c(10.5, 8.3, 9.1, 10.2, 8.0),
  end_weight = c(10.2, 8.1, 8.9, 9.9, 7.8)
)

# Process matrices
matrices <- matrix_process(toy_data, type = "feed",
                          id_col = "animal", start_col = "start",
                          end_col = "end", bin_col = "bin",
                          start_weight_col = "start_weight",
                          end_weight_col = "end_weight",
                          bins_feed = 1:3)

# Analyze neighbor patterns with linear layout
neighbor_results <- synch_neighbor_analysis(
  matrices, 
  bin_layout = "1-2-3",
  type = "feed",
  id_col = "animal"
)