calibrar_demo function

Demos for the calibrar package

Creates demo files able to be processed for a full calibration using the calibrar package

calibrar_demo(path = NULL, model = NULL, ...)

Arguments

• path: Path to create the demo files
• model: Model to be used in the demo files, see details.
• ...: Additional parameters to be used in the construction of the demo files.

Returns

A list with the following elements: - path: Path were the files were saved

• par: Real value of the parameters used in the demo

• setup: Path to the calibration setup file

• guess: Values to be provided as initial guess to the calibrate function

• lower: Values to be provided as lower bounds to the calibrate function

• upper: Values to be provided as upper bounds to the calibrate function

• phase: Values to be provided as phases to the calibrate function

• constants: Constants used in the demo, any other variable not listed here.

• value: NA, set for compatibility with summary methods.

• time: NA, set for compatibility with summary methods.

• counts: NA, set for compatibility with summary methods.

Details

Current implemented models are:

• PoissonMixedModel: Poisson Autoregressive Mixed model for the dynamics of a population in different sites:

   where $\mu_{i, t}$ is the size of the population in site $i$ at year $t$, $X_{i, t}$ is the value of an environmental variable in site $i$ at year $t$. The parameters to estimate were $\alpha$, $\beta$, and $\gamma_t$, the random effects for each year, $\gamma_t \sim N(0,\sigma^2)$, and the initial population at each site $\mu_{i, 0}$. We assumed that the observations $N_{i,t}$ follow a Poisson distribution with mean $\mu_{i, t}$.

• PredatorPrey: Lotka Volterra Predator-Prey model. The model is defined by a system of ordinary differential equations for the abundance of prey $N$ and predator $P$:

   The parameters to estimate are the prey’s growth rate $r$, the predator’s mortality rate $l$, the carrying capacity of the prey $K$ and $\alpha$
   
   and $\gamma$ for the predation interaction. Uses `deSolve` package for numerical solution of the ODE system.

• SIR: Susceptible-Infected-Recovered epidemiological model. The model is defined by a system of ordinary differential equations for the number of susceptible $S$, infected $I$ and recovered $R$ individuals:

   The parameters to estimate are the average number of contacts per person per time $\beta$ and the instant probability of an infectious individual recovering $\gamma$. Uses `deSolve` package for numerical solution of the ODE system.

• IBMLotkaVolterra: Stochastic Individual Based Model for Lotka-Volterra model. Uses ibm package for the simulation.

Examples

## Not run:

summary(ahr)
set.seed(880820)
path = NULL # NULL to use the current directory
# create the demonstration files
demo = calibrar_demo(path=path, model="PredatorPrey", T=100) 
# get calibration information
calibration_settings = calibration_setup(file = demo$setup)
# get observed data
observed = calibration_data(setup = calibration_settings, path=demo$path)
# Defining 'run_model' function
run_model = calibrar:::.PredatorPreyModel
# real parameters
cat("Real parameters used to simulate data\n")
print(unlist(demo$par)) # parameters are in a list
# objective functions
obj  = calibration_objFn(model=run_model, setup=calibration_settings, observed=observed, T=demo$T)
obj2 = calibration_objFn(model=run_model, setup=calibration_settings, observed=observed, 
T=demo$T, aggregate=TRUE)
cat("Starting calibration...\n")
cat("Running optimization algorithms\n", "\t")
cat("Running optim AHR-ES\n")
ahr = calibrate(par=demo$guess, fn=obj, lower=demo$lower, upper=demo$upper, phases=demo$phase)
summary(ahr)
## End(Not run)

References

Oliveros-Ramos and Shin (2014)

Author(s)

Ricardo Oliveros--Ramos