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).
Author
Marina Papadopoulou m.papadopoulou.rug@gmail.com
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
)
#> Given thresholds return a total of:
#> 5 events, over 0.166666666666667 min.
#> Calculating metrics for each event, this might take a while...