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Analyze pair-wise co-occurrence patterns

Source: R/synch_pair_analysis.R
synch_pair_analysis.Rd

Analyzes how long each pair of animals spend feeding or drinking together simultaneously, regardless of their spatial location. For each pair of animals, calculates the number of bouts they spent together, total time together, and average duration per bout.

Usage

synch_pair_analysis(
  matrix_data,
  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_animal2 component with Time column and animal ID columns.

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 together

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

  • avg_duration: Average duration per bout for each pair

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

Examples

# Create toy data with multiple animals
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 pair-wise co-occurrence
pair_results <- synch_pair_analysis(matrices, type = "feed",
                                   id_col = "animal")