calibrar_demo {calibrar} | R Documentation |
Demos for the calibrar package
Description
Creates demo files able to be processed for a full calibration using the calibrar package
Usage
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. |
Details
Current implemented models are:
- PoissonMixedModel
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 sitei
at yeart
,X_{i, t}
is the value of an environmental variable in sitei
at yeart
. 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 observationsN_{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 $P$:
\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 ratel
, the carrying capacity of the preyK
and\alpha
and\gamma
for the predation interaction. UsesdeSolve
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:
\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
. UsesdeSolve
package for numerical solution of the ODE system.- IBMLotkaVolterra
Stochastic Individual Based Model for Lotka-Volterra model. Uses
ibm
package for the simulation.
Value
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. |
Author(s)
Ricardo Oliveros–Ramos
References
Oliveros-Ramos and Shin (2014)
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)