Create a stochastic model

This function provides a formatted and colorized summary of a stochastic model, including initial values, parameters, and a list of reactions with their transitions and rate function arguments.

Usage

stochastic_model(reactions, sim_scenario)

# S3 method for class 'stochastic_model'
print(x, ...)

run_sim(x, tf = NULL)

update_parameters(x, ..., params = list())

update_state(x, state)

interpolate_run_by_day(x)

Arguments

reactions

list of state transitions. Each reaction is itself a list that requires a transition vector and a rate vector

sim_scenario

list that defines the scenario including params, initial_states and sim_args that must at least include simulation time T

x

A matrix or data frame with a numeric column named "time" and one or more state columns with arbitrary names. output of run

...

parameters to update

tf

maximum time

params

parameters to update passed in as a list

state

data.frame

Value

Invisibly returns the x object.

An object of class stochastic_model.

An object of class stochastic_model.

A tibble() with one row per whole day and interpolated values for each state.

Methods (by generic)

• print(stochastic_model): Print method for stochastic_model objects

Functions

• run_sim(): run stochastic model using adaptive tau set algorithm

• update_parameters(): update parameters for a stochastic_model

• update_state(): update initial state for a stochastic_model

• interpolate_run_by_day(): Interpolate State Trajectories to Whole Days

  Converts a time-series data frame or matrix with continuous time points and one or more state columns into a tibble() with daily interpolated values.

Examples

reactions <- list(
  infection = list(
    transition = c("I" = +1),
    rate = function(x,p,t){p$beta})
)
example_scenario <- list(
  params = list(beta = 0.1),
  initial_states = list(I = 0),
  sim_args = list(T = 10)
)
stochastic_model(reactions,example_scenario)
#> Stochastic Model 
#> ================== 
#> 
#> Initial Values:
#>   I : 0
#> Parameters:
#>   beta : 0.1
#> Simulation arguments:
#>   T : 10
#> Reactions:
#>  - infection:
#>    Transition: [I +1]
#> 

reactions <- list(
  infection = list(
    transition = c("I" = +1),
    rate = function(x,p,t){p$beta})
)
example_scenario <- list(
  params = list(beta = 0.1),
  initial_states = list(I = 0),
  sim_args = list(T = 10)
)
sm <- stochastic_model(reactions,example_scenario)
sm <- update_parameters(sm,beta=0.2)

mat <- cbind(
  time = c(0.1, 0.5, 1.2, 2.4, 3.8),
  S = c(1000, 900, 800, 600, 500),
  I = c(1, 10, 50, 100, 90)
)
interpolate_run_by_day(mat)
#> # A tibble: 5 × 3
#>    time     S     I
#>   <int> <dbl> <dbl>
#> 1     0   NA   NA  
#> 2     1  829.  38.6
#> 3     2  667.  83.3
#> 4     3  557.  95.7
#> 5     4   NA   NA