Create TMB Model Specification — mp_tmb_model

Specify a simulation model in the TMB engine. A detailed explanation of this function is covered in vignette("quickstart").

Usage

mp_tmb_model_spec(
  before = list(),
  during = list(),
  after = list(),
  default = list(),
  integers = list(),
  must_save = character(),
  must_not_save = character(),
  sim_exprs = character(),
  state_update = c("euler", "rk4", "euler_multinomial", "hazard", "rk4_old")
)

Arguments

before: List of formulas to be evaluated (in the order provided) before the simulation loop begins. These formulas must be standard two-sided R formula objects. See details below for the rules for these formulas.
during: List of formulas or calls to flow functions (e.g., mp_per_capita_flow) to be evaluated at every iteration of the simulation loop.
after: List of formulas to be evaluated (in the order provided) before the simulation loop begins. These formulas must be standard two-sided R formula objects. See details below for the rules for these formulas.
default: Named list of objects, each of which can be coerced into a numeric matrix. The names refer to variables that appear in before, during, and after.
integers: Named list of vectors that can be coerced to integer vectors. These integer vectors can be used by name in model formulas to provide indexing of matrices and as grouping factors in group_sums.
must_save: Character vector of the names of variables that must have their values stored at every iteration of the simulation loop. For example, a variable that you do not want to be returned, but that impacts dynamics with a time lag, must be saved and therefore must be in this list.
must_not_save: Character vector of the names of variables that must not have their values stored at every iteration of the simulation loop. For example, the user may ask to return a very large matrix that would create performance issues if stored at each iteration. The creator of the model can mark such variables making it impossible for the user of the model to save their full simulation history.
sim_exprs: Character vector of the names of before, during, and after expressions that must only be evaluated when simulations are being produced and not when the objective function is being evaluated. For example, expressions that generate stochasticity should be listed in sim_exprs because TMB objective functions must be continuous.
state_update: Optional character vector for how to update the state variables when it is relevant. Options include "euler" (the default), "rk4", and "euler_multinomial".

Details

Expressions in the before, during, and after lists can be standard R formula objects for defining variables in the model. These formulas must have a left hand side that gives the name of the (possibly matrix-valued) variable being updated, and a right hand side giving an expression containing only (1) the names of quantities in the model, (2) numerical literals (e.g., 3.14), or (3) functions defined in the TMB engine (described in engine_functions). For example, the expression N ~ S + I + R updates the value of N to be the sum of the variables S, I, and R.

Names can be provided for the components of the before, during, and after lists, and these names do not have to be unique. These names are used by the sim_exprs argument.

Examples

## A simple SI model.
spec = mp_tmb_model_spec(
    during = mp_per_capita_flow("S", "I", "beta * I / N", "infection")
  , default = list(N = 100, S = 99, I = 1, beta = 0.2)
)
(spec 
  |> mp_simulator(time_steps = 5L, output = "infection") 
  |> mp_trajectory()
)
#>      matrix time row col     value
#> 1 infection    1   0   0 0.1980000
#> 2 infection    2   0   0 0.2367296
#> 3 infection    3   0   0 0.2828290
#> 4 infection    4   0   0 0.3376117
#> 5 infection    5   0   0 0.4025866

## The `~` can be used for flexibly defining dynamical variables.
spec2 = mp_tmb_model_spec(
    during = list(
          force_of_infection ~ beta * I / N
        , mp_per_capita_flow("S", "I", "force_of_infection", "infection")
    )
  , default = list(N = 100, S = 99, I = 1, beta = 0.2)
)
(spec2
  |> mp_simulator(time_steps = 5L, output = "force_of_infection") 
  |> mp_trajectory()
)
#>               matrix time row col       value
#> 1 force_of_infection    1   0   0 0.002000000
#> 2 force_of_infection    2   0   0 0.002396000
#> 3 force_of_infection    3   0   0 0.002869459
#> 4 force_of_infection    4   0   0 0.003435117
#> 5 force_of_infection    5   0   0 0.004110341

## The `before` argument can be used to pre-compute quantities before
## the simulation loop begins. Here we compute `S` from `N` and `I`,
## instead of specifying `S` in the `default` list. The potential
## benefit here is that one could make `I` a parameter to be fitted,
## while ensuring consistent values for `S`.
spec3 = mp_tmb_model_spec(
    before = S ~ N - I
  , during = mp_per_capita_flow("S", "I", "beta * I / N", "infection")
  , default = list(N = 100, I = 1, beta = 0.2)
)
(spec3 
  |> mp_simulator(time_steps = 5L, output = "infection") 
  |> mp_trajectory()
)
#>      matrix time row col     value
#> 1 infection    1   0   0 0.1980000
#> 2 infection    2   0   0 0.2367296
#> 3 infection    3   0   0 0.2828290
#> 4 infection    4   0   0 0.3376117
#> 5 infection    5   0   0 0.4025866