Description Details Author(s) References See Also Examples

Package that provides Synthetic Likelihood methods for intractable likelihoods. The package is meant to be as general purpose as possible: as long as you are able to simulate data from your model you should be able to fit it.

Package: | synlik |

Type: | Package |

Version: | 0.1 |

Date: | 2014-03-20 |

License: | GPL (>=2) |

The package allows users to create objects of class `synlik`

(S4), which are essentially constituted of a `simulator`

function and
a function (`summaries`

) that transforms the data into summary statistics. The `simulator`

can output any kind of data (vector, list, etc)
and this will be passed directly to the `summaries`

function. This allow the package to fit a large variety of models.

Once the model of interest has been set up as a `synlik`

object, it is possible several methods on it. The function most useful function is `slik`

, which can be used to evaluate the synthetic likelihood. The `slice.synlik`

function allows to obtain and plot slices of the synthetic likelihood with respect to model parameters. It is possible to simulate data or statistics from the model using the generic `simulate`

, and to check the normality of the statistics using the `checkNorm`

function. Unknow parameters can be estimated by MCMC, through the `smcmc`

function. This function will return an object of class `smcmc`

(S4), which contains all the inputs and results of the MCMC procedure.

Many functions in the package support parallel simulation on multiple cores.

Matteo Fasiolo and Simon Wood

Maintainer: Matteo Fasiolo <[email protected]>

Simon N Wood. Statistical inference for noisy nonlinear ecological dynamic systems. Nature, 466(7310):1102–1104, 2010.

For some examples see the Vignettes (type `vignette("synlik")`

).

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## Not run:
#### Here I put a simple example,
#### if you want to see more type: vignette("synlik")
## End(Not run)
#### Create synlik object
ricker_sl <- synlik(simulator = rickerSimul,
summaries = rickerStats,
param = c(logR = 3.8, logSigma = log(0.3), logPhi = log(10)),
extraArgs = list("nObs" = 50, "nBurn" = 50),
plotFun = function(input, ...){
plot(drop(input), type = 'l', ylab = "Pop", xlab = "Time", ...)
}
)
#### Simulate from the object
ricker_sl@data <- simulate(ricker_sl)
ricker_sl@extraArgs$obsData <- ricker_sl@data
#### Simulate statistics (each row is a vector of statistics)
simulate(ricker_sl, seed = 523, nsim = 10, stats = TRUE)
#### Plotting the data
plot(ricker_sl)
#### Checking multivariate normality of the statistics
checkNorm(ricker_sl)
#### Evaluate the likelihood
set.seed(4234)
slik(ricker_sl,
param = c(logR = 3.8, logSigma = log(0.3), logPhi = log(10)),
nsim = 1e3)
#### Plotting a slice of the log-Likelihood possibly using multiple cores
slice(object = ricker_sl,
ranges = list("logR" = seq(3.5, 3.9, by = 0.02),
"logPhi" = seq(2, 2.6, by = 0.02),
"logSigma" = seq(-2, -0.5, by = 0.05)),
param = c(logR = 3.8, logSigma = log(0.3), logPhi = log(10)),
nsim = 500, multicore = FALSE)
#### MCMC estimation possibly using multiple cores
set.seed(4235)
ricker_sl <- smcmc(ricker_sl,
initPar = c(3.2, -1, 2.6),
niter = 50,
burn = 3,
priorFun = function(input, ...) 0,
propCov = diag(c(0.1, 0.1, 0.1))^2,
nsim = 1e3,
multicore = FALSE)
# Continue with additional 50 iterations
ricker_sl <- continue(ricker_sl, niter = 50)
# Plotting results on transformed scale (exponential)
trans <- rep("exp", 3)
names(trans) <- names(ricker_sl@param)
plot(ricker_sl)
``` |

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