Type:	Package
Title:	Modelling Structured Dynamical Systems in 'greta'
Version:	0.2.2
Description:	A 'greta' extension for analysing transition matrices and ordinary differential equations representing dynamical systems. Provides functions for analysing transition matrices by iteration, and solving ordinary differential equations. This is an extension to the 'greta' software, Golding (2019) <doi:10.21105/joss.01601>.
License:	Apache License (≥ 2)
URL:	https://github.com/greta-dev/greta.dynamics, https://greta-dev.github.io/greta.dynamics/
BugReports:	https://github.com/greta-dev/greta.dynamics/issues
Imports:	cli (≥ 3.6.3), glue, rlang (≥ 1.1.4), tensorflow (≥ 1.14.0)
Depends:	greta (≥ 0.5.0), R (≥ 4.1.0)
Suggests:	covr, knitr, rmarkdown, spelling, testthat (≥ 3.1.0), deSolve, abind
VignetteBuilder:	knitr
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en-GB
RoxygenNote:	7.3.2
SystemRequirements:	Python (>= 3.7.0) with header files and shared library; TensorFlow (>= v2.0.0; https://www.tensorflow.org/); TensorFlow Probability (v0.8.0; https://www.tensorflow.org/probability/)
NeedsCompilation:	no
Packaged:	2024-11-14 01:39:10 UTC; nick
Author:	Nick Golding [aut, cph], Nicholas Tierney [aut, cre]
Maintainer:	Nicholas Tierney <nicholas.tierney@gmail.com>
Repository:	CRAN
Date/Publication:	2024-11-14 04:50:02 UTC

greta.dynamics: a greta extension for modelling dynamical systems

Description

an extension to greta with functions for simulating dynamical systems, defined by of ordinary differential equations (see ode_solve()) or transition matrices (iterate_matrix()).

Author(s)

Maintainer: Nicholas Tierney nicholas.tierney@gmail.com (ORCID)

Authors:

• Nick Golding nick.golding.research@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

• https://github.com/greta-dev/greta.dynamics

• https://greta-dev.github.io/greta.dynamics/

• Report bugs at https://github.com/greta-dev/greta.dynamics/issues

iterate dynamic transition functions

Description

Calculate the stable population size for a stage-structured dynamical system, encoded by a transition function, the value of which changes at each iteration, given by function of the previous state: state[t] = f(state[t-1]).

Usage

iterate_dynamic_function(
  transition_function,
  initial_state,
  niter,
  tol,
  ...,
  parameter_is_time_varying = c(),
  state_limits = c(-Inf, Inf)
)

Arguments

Details

Like iterate_dynamic_matrix this converges to absolute population sizes. The convergence criterion is therefore based on growth rates converging on 0.

The greta array returned by transition_function must have the same dimension as the state input and initial_state should be shaped accordingly, as detailed in iterate_matrix.

To ensure the matrix is iterated for a specific number of iterations, you can set that number as niter, and set tol to 0 or a negative number to ensure that the iterations are not stopped early.

Value

a named list with four greta arrays:

• stable_state a vector or matrix (with the same dimensions as initial_state) giving the state after the final iteration.

• all_states an n x m x niter matrix of the state values at each iteration. This will be 0 for all entries after iterations.

• converged an integer scalar indicating whether all the matrix iterations converged to a tolerance less than tol (1 if so, 0 if not) before the algorithm finished.

• iterations a scalar of the maximum number of iterations completed before the algorithm terminated. This should match niter if converged is FALSE

Note

because greta vectorises across both MCMC chains and the calculation of greta array values, the algorithm is run until all chains (or posterior samples), sites and stages have converged to stable growth. So a single value of both converged and iterations is returned, and the value of this will always have the same value in an mcmc.list object. So inspecting the MCMC trace of these parameters will only tell you whether the iteration converged in all posterior samples, and the maximum number of iterations required to do so across all these samples

iterate dynamic transition matrices

Description

Calculate the stable population size for a stage-structured dynamical system, encoded by a transition matrix, the value of which changes at each iteration, given by function of the previous state: state[t] = f(state[t-1]) %*% state[t-1].

Usage

iterate_dynamic_matrix(
  matrix_function,
  initial_state,
  niter,
  tol,
  ...,
  state_limits = c(-Inf, Inf)
)

Arguments

Details

Because iterate_matrix iterates with a static transition matrix, it converges to a stable growth rate and relative population sizes for a dynamical system. iterate_dynamic_matrix instead uses a matrix which changes at each iteration, and can be dependent on the population sizes after the previous iteration, and the iteration number. Because this can encode density-dependence, the dynamics can converge to absolute population sizes. The convergence criterion is therefore based on growth rates converging on 0.

As in iterate_matrix, the greta array returned by matrix_function can either be a square matrix, or a 3D array representing (on the first dimension) n different matrices. initial_state should be shaped accordingly, as detailed in iterate_matrix.

To ensure the matrix is iterated for a specific number of iterations, you can set that number as niter, and set tol to 0 or a negative number to ensure that the iterations are not stopped early.

Value

a named list with four greta arrays:

• stable_state a vector or matrix (with the same dimensions as initial_state) giving the state after the final iteration.

• all_states an n x m x niter matrix of the state values at each iteration. This will be 0 for all entries after iterations.

• converged an integer scalar indicating whether all the matrix iterations converged to a tolerance less than tol (1 if so, 0 if not) before the algorithm finished.

• iterations a scalar of the maximum number of iterations completed before the algorithm terminated. This should match niter if converged is FALSE

Note

because greta vectorises across both MCMC chains and the calculation of greta array values, the algorithm is run until all chains (or posterior samples), sites and stages have converged to stable growth. So a single value of both converged and iterations is returned, and the value of this will always have the same value in an mcmc.list object. So inspecting the MCMC trace of these parameters will only tell you whether the iteration converged in all posterior samples, and the maximum number of iterations required to do so across all these samples

iterate transition matrices

Description

Calculate the intrinsic growth rate(s) and stable stage distribution(s) for a stage-structured dynamical system, encoded as ⁠state_t = matrix \%*\% state_tm1⁠.

Usage

iterate_matrix(
  matrix,
  initial_state = rep(1, ncol(matrix)),
  niter = 100,
  tol = 1e-06
)

Arguments

Details

iterate_matrix can either act on a single transition matrix and initial state (if matrix is 2D and initial_state is a column vector), or it can simultaneously act on n different matrices and/or n different initial states (if matrix and initial_state are 3D arrays). In the latter case, the first dimension of both objects should be the batch dimension n.

To ensure the matrix is iterated for a specific number of iterations, you can set that number as niter, and set tol to 0 or a negative number to ensure that the iterations are not stopped early.

Value

a named list with five greta arrays:

• lambda a scalar or vector giving the ratio of the first stage values between the final two iterations.

• stable_state a vector or matrix (with the same dimensions as initial_state) giving the state after the final iteration, normalised so that the values for all stages sum to one.

• all_states an n x m x niter matrix of the state values at each iteration. This will be 0 for all entries after iterations.

• converged an integer scalar or vector indicating whether the iterations for each matrix have converged to a tolerance less than tol (1 if so, 0 if not) before the algorithm finished.

• iterations a scalar of the maximum number of iterations completed before the algorithm terminated. This should match niter if converged is FALSE.

Note

because greta vectorises across both MCMC chains and the calculation of greta array values, the algorithm is run until all chains (or posterior samples), sites and stages have converged to stable growth. So a single value of both converged and iterations is returned, and the value of this will always have the same value in an mcmc.list object. So inspecting the MCMC trace of these parameters will only tell you whether the iteration converged in all posterior samples, and the maximum number of iterations required to do so across all these samples

Examples

## Not run: 
# simulate from a probabilistic 4-stage transition matrix model
k <- 4

# component variables
# survival probability for all stages
survival <- uniform(0, 1, dim = k)
# conditional (on survival) probability of staying in a stage
stasis <- c(uniform(0, 1, dim = k - 1), 1)
# marginal probability of staying/progressing
stay <- survival * stasis
progress <- (survival * (1 - stay))[1:(k - 1)]
# recruitment rate for the largest two stages
recruit <- exponential(c(3, 5))

# combine into a matrix:
tmat <- zeros(k, k)
diag(tmat) <- stay
progress_idx <- row(tmat) - col(tmat) == 1
tmat[progress_idx] <- progress
tmat[1, k - (1:0)] <- recruit

# analyse this to get the intrinsic growth rate and stable state
iterations <- iterate_matrix(tmat)
iterations$lambda
iterations$stable_distribution
iterations$all_states

# Can also do this simultaneously for a collection of transition matrices
k <- 2
n <- 10
survival <- uniform(0, 1, dim = c(n, k))
stasis <- cbind(uniform(0, 1, dim = n), rep(1, n))
stay <- survival * stasis
progress <- (survival * (1 - stasis))[, 1]
recruit_rate <- 1 / seq(0.1, 5, length.out = n)
recruit <- exponential(recruit_rate, dim = n)
tmats <- zeros(10, 2, 2)
tmats[, 1, 1] <- stasis[, 1]
tmats[, 2, 2] <- stasis[, 2]
tmats[, 2, 1] <- progress
tmats[, 1, 2] <- recruit

iterations <- iterate_matrix(tmats)
iterations$lambda
iterations$stable_distribution
iterations$all_states

## End(Not run)

solve ODEs

Description

Solve a system of ordinary differential equations.

Usage

ode_solve(derivative, y0, times, ..., method = c("dp", "bdf"))

Arguments

Value

greta array

Examples

## Not run: 
# replicate the Lotka-Volterra example from deSolve
library(deSolve)
LVmod <- function(Time, State, Pars) {
  with(as.list(c(State, Pars)), {
    Ingestion <- rIng * Prey * Predator
    GrowthPrey <- rGrow * Prey * (1 - Prey / K)
    MortPredator <- rMort * Predator

    dPrey <- GrowthPrey - Ingestion
    dPredator <- Ingestion * assEff - MortPredator

    return(list(c(dPrey, dPredator)))
  })
}

pars <- c(
  rIng = 0.2, # /day, rate of ingestion
  rGrow = 1.0, # /day, growth rate of prey
  rMort = 0.2, # /day, mortality rate of predator
  assEff = 0.5, # -, assimilation efficiency
  K = 10
) # mmol/m3, carrying capacity

yini <- c(Prey = 1, Predator = 2)
times <- seq(0, 30, by = 1)
out <- ode(yini, times, LVmod, pars)

# simulate observations
jitter <- rnorm(2 * length(times), 0, 0.1)
y_obs <- out[, -1] + matrix(jitter, ncol = 2)

# ~~~~~~~~~
# fit a greta model to infer the parameters from this simulated data

# greta version of the function
lotka_volterra <- function(y, t, rIng, rGrow, rMort, assEff, K) {
  Prey <- y[1, 1]
  Predator <- y[1, 2]

  Ingestion <- rIng * Prey * Predator
  GrowthPrey <- rGrow * Prey * (1 - Prey / K)
  MortPredator <- rMort * Predator

  dPrey <- GrowthPrey - Ingestion
  dPredator <- Ingestion * assEff - MortPredator

  cbind(dPrey, dPredator)
}

# priors for the parameters
rIng <- uniform(0, 2) # /day, rate of ingestion
rGrow <- uniform(0, 3) # /day, growth rate of prey
rMort <- uniform(0, 1) # /day, mortality rate of predator
assEff <- uniform(0, 1) # -, assimilation efficiency
K <- uniform(0, 30) # mmol/m3, carrying capacity

# initial values and observation error
y0 <- uniform(0, 5, dim = c(1, 2))
obs_sd <- uniform(0, 1)

# solution to the ODE
y <- ode_solve(lotka_volterra, y0, times, rIng, rGrow, rMort, assEff, K)

# sampling statement/observation model
distribution(y_obs) <- normal(y, obs_sd)

# we can use greta to solve directly, for a fixed set of parameters (the true
# ones in this case)
values <- c(
  list(y0 = t(1:2)),
  as.list(pars)
)
vals <- calculate(y, values = values)[[1]]
plot(vals[, 1] ~ times, type = "l", ylim = range(vals))
lines(vals[, 2] ~ times, lty = 2)
points(y_obs[, 1] ~ times)
points(y_obs[, 2] ~ times, pch = 2)

# or we can do inference on the parameters:

# build the model (takes a few seconds to define the tensorflow graph)
m <- model(rIng, rGrow, rMort, assEff, K, obs_sd)

# compute MAP estimate
o <- opt(m)
o

## End(Not run)