ode_solve {greta.dynamics}

solve ODEs

Description

Solve a system of ordinary differential equations.

Usage

ode_solve(derivative, y0, times, ..., method = c("ode45", "rk4", "midpoint"))

Arguments

derivative

a derivative function. The first two arguments must be 'y' and 't', the state parameter and scalar timestep respectively. The remaining parameters must be named arguments representing (temporally static) model parameters. Variables and distributions cannot be defined in the function.

y0

a greta array for the value of the state parameter y at time 0

times

a column vector of times at which to evaluate y

...

named arguments giving greta arrays for the additional (fixed) parameters

method

which solver to use. "ode45" uses adaptive step sizes, whilst "rk4" and "midpoint" use the fixed grid defined by times; they may be faster but less accurate than "ode45".

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)