col_motion_metrics: Collective Motion Metrics

View source: R/wrap_col_motion_metrics.R

col_motion_metricsR Documentation

Collective Motion Metrics

Description

This function calculates metrics of collective motion across sets and events.

Usage

col_motion_metrics(
  timeseries_data,
  global_metrics,
  step2time = 1,
  verbose = TRUE,
  speed_lim = NA,
  pol_lim = NA,
  noise_thresh = 0
)

Arguments

timeseries_data

A data frame with time series of individual's positional data through time with nearest neighbor analysis conducted

global_metrics

A data frame with the global metrics timeseries.

step2time

Numeric, the sampling frequency of the dataset (the relation between a time step and real time in seconds).

verbose

Logical, whether to post updates on progress.

speed_lim

Numeric, the threshold of speed for the definition of an event. For more info see: pick_threshold.

pol_lim

Numeric, the threshold of polarization for the definition of an event. For more info see: pick_threshold.

noise_thresh

Numeric, the limit of time difference between consecutive events to be considered the same event. Default value is 0 (no event merging).

Value

A dataframe with metrics of collective motion per event.

Author(s)

Marina Papadopoulou m.papadopoulou.rug@gmail.com

See Also

define_events, group_metrics, pairwise_metrics

Examples


## A dataframe with group timeseries
g_df <- data.frame(
 t = as.POSIXct(1:25, origin = "2024-03-18 14:56:05"),
 set = rep(1, 25),
 pol = c(rnorm(25)),
 pol_av = c(rnorm(25)),
 speed = c(rnorm(25)),
 speed_av = c(rnorm(25)),
 shape = c(rnorm(25)),
 event = rep(1, 25),
 N = rep(3, 25)
 )

## A dataframe with individual timeseries
p_df <- data.frame(
 t = as.POSIXct(rep(1:25, 3), origin = "2024-03-18 14:56:05"),
 set = rep(1, 75),
 nnd = c(rnorm(75)),
 bangl = runif(75, 0, pi),
 id = c(rep(1, 25), rep(2, 25), rep(3, 25)),
 nn_id = c(
  sample(c(2,3), 25, replace = TRUE),
  sample(c(1,3), 25, replace = TRUE),
  sample(c(2,1), 25, replace = TRUE)),
 event = rep(1, 75)
)
p_df$only_time <- format(p_df$t, "%H:%M:%OS2")

metrics <- col_motion_metrics(
 timeseries_data = p_df,
 global_metrics = g_df,
 step2time = 1,
 speed_lim = 0,
 pol_lim = 0,
 noise_thresh = 1
)


swaRmverse documentation built on May 29, 2024, 8:57 a.m.