Creates demo files able to be processed for a full calibration using the calibrar package
calibrar_demo(path = NULL, model = NULL, ...)Path to create the demo files
Model to be used in the demo files, see details.
Additional parameters to be used in the construction of the demo files.
A list with the following elements:
Path were the files were saved
Real value of the parameters used in the demo
Path to the calibration setup file
Values to be provided as initial guess to the calibrate function
Values to be provided as lower bounds to the calibrate function
Values to be provided as upper bounds to the calibrate function
Values to be provided as phases to the calibrate function
Constants used in the demo, any other variable not listed here.
NA, set for compatibility with summary methods.
NA, set for compatibility with summary methods.
NA, set for compatibility with summary methods.
Current implemented models are:
Poisson Autoregressive Mixed model for the dynamics of a population in different sites: $$log(\mu_{i, t+1}) = log(\mu_{i, t}) + \alpha + \beta X_{i, t} + \gamma_t$$ 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 \(\mu_{i, t}\).
Lotka Volterra Predator-Prey model. The model is defined
by a system of ordinary differential equations for the abundance of prey $N$ and predator $P$:
$$\frac{dN}{dt} = rN(1-N/K)-\alpha NP$$
$$\frac{dP}{dt} = -lP + \gamma\alpha NP$$
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.
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:
$$\frac{dS}{dt} = -\beta S I/N$$
$$\frac{dI}{dt} = \beta S I/N -\gamma I$$
$$\frac{dR}{dt} = \gamma I$$
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.
Stochastic Individual Based Model for Lotka-Volterra model. Uses ibm package for the simulation.
Oliveros-Ramos and Shin (2014)
if (FALSE) {
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)
}