Transform trace to comprehensive data.frame — unpack

This function will take a trace and return a data.frame containing all information contained within a typical time-series (cfr. time_series) and with all the input that should be provided to the utility functions. This is therefore the primary function to use if you want to go from a trace to a data.frame that can be used in M4MA-based estimations.

Usage

unpack_trace(
  trace,
  velocities = matrix(rep(c(1.5, 1, 0.5), each = 11), ncol = 3),
  orientations = matrix(rep(c(72.5, 50, 32.5, 20, 10, 0, 350, 340, 327.5, 310, 287.5),
    times = 3), ncol = 3),
  stay_stopped = TRUE,
  time_step = 0.5,
  cpp = TRUE
)

Arguments

trace: List of objects of the state-class
velocities: Numeric matrix containing the change in speed for an agent whenever they move to the respective cell of this matrix. Is used to create the cell positions that the agent might move to. Defaults to a matrix in which the columns contain 1.5 (acceleration), 1 (maintenance of speed), and 0.5 (deceleration).
orientations: Numeric matrix containing the change in direction for an agent whenever they move to the respective cell of this matrix. Is used to create the cell positions that the agent might move to. Defaults to a matrix in which the rows contain 72.5, 50, 32.5, 20, 10, 0, 350, 340, 327.5, 310, 287.5 (note that the larger angles are actually the negative symmetric versions of the smaller angles).
stay_stopped: Logical denoting whether agents will predict others that are currently not moving to remain immobile in the next iteration. Defaults to TRUE.
time_step: Numeric denoting the time between each iteration. Defaults to 0.5 (the same as in simulate).
cpp: Logical denoting whether to use the Rcpp (TRUE) or R (FALSE) version of this function. Defaults to TRUE.

Examples

# This is my example