tidy_trajectories {isotracer}

Build a tidy table with the trajectories for each iteration

Description

If neither n_per_chain and n are provided, all iterations are used.

Usage

tidy_trajectories(
  nm,
  mcmc,
  n_per_chain = NULL,
  n = NULL,
  n_grid = 64,
  dt = NULL,
  grid_size = NULL,
  at = NULL,
  end = NULL,
  use_cache = TRUE,
  cores = NULL
)

Arguments

nm

A networkModel object.

mcmc

The corresponding output from run_mcmc.

n_per_chain

Integer, number of iterations randomly drawn per chain. Note that iterations are in sync across chains (in practice, random iterations are chosen, and then parameter values extracted for those same iterations from all chains).

n

Integer, number of iterations randomly drawn from mcmc. Note that iterations are *not* drawn in sync across chains in this case (use n_per_chain if you need to have the same iterations taken across all chains).

n_grid

Size of the time grid used to calculate trajectories

dt, grid_size

Time step size or grid points, respectively.

at

Timepoints at which the predictions should be returned.

end

Final timepoint used in the projections.

use_cache

Boolean, use cache for faster calculations?

cores

Number of cores to use for parallel calculations. Default is NULL, which means to use the value stored in options()[["mc.cores"]] (or 1 if this value is not set).

Details

Warning: This function is still maturing and its interface and output might change in the future.

Value

A tidy table containing the mcmc iterations (chain, iteration, parameters), the grouping variables from the network model and the trajectories.

Examples

tt <- tidy_trajectories(aquarium_mod, aquarium_run, n = 10, cores = 2)
tt