Description Slots Author(s) References Examples
Object representing the results of MCMC estimation on an
object of class synlik
, from which it inherits.
Vector of
initial parameters where the MCMC chain will start
(numeric
).
Number of MCMC iterations
(integer
).
Number of simulations from
the simulator at each step of the MCMC algorithm
(integer
).
Number of initial MCMC
iterations that are discarded (integer
).
Function that takes a vector of
parameters as input and the log-density of the prior as
output. If the output is not finite the proposed point
will be discarded. (function
). The function needs
to have signature fun(x, ...)
, where x
represents the input parameters (function
).
Matrix representing the covariance matrix
to be used to perturb the parameters at each step of the
MCMC chain (matrix
).
Target
rate for the adaptive MCMC sampler. Should be in (0, 1),
default is NULL (no adaptation). The adaptation uses the
approach of Vihola (2011). (numeric
)
If TRUE the synthetic likelihood will be
evaluated at the current and proposed positions in the
parameter space (thus doubling the computational effort).
If FALSE the likelihood of the current position won't be
re-estimated (logical
).
If TRUE
the object@simulator
and object@summaries
functions will be executed in parallel. That is the nsim
simulations will be divided in multiple cores
(logical
).
Number of cores to use
if multicore == TRUE (integer
).
Acceptance rate of the MCMC chain, between
0 and 1 (numeric
).
Matrix of size
niter by length(initPar) where the i-th row contains the
position of the MCMC algorithm in the parameter space at
the i-th (matrix
).
Vector of
niter elements where the i-th element is contains the
estimate of the synthetic likelihood at the i-th
iteration (numeric
).
Control parameters used by the MCMC sampler:
theta
= controls the speed of adaption.
Should be between 0.5 and 1. A lower gamma leads to
faster adaption.
adaptStart
= iteration
where the adaption starts. Default 0.
adaptStop
= iteration where the adaption
stops. Default burn + niter
saveFile
= path to the file where the intermediate results will be
stored (ex: "~/Res.RData").
saveFreq
=
frequency with which the intermediate results will be
saved on saveFile
. Default 100.
verbose
= if TRUE
intermediate
posterior means will be printer.
verbFreq
=
frequency with which the intermediate posterior means
will be printer. Default 500.
Matteo Fasiolo <matteo.fasiolo@gmail.com>
Vihola, M. (2011) Robust adaptive Metropolis algorithm with coerced acceptance rate. Statistics and Computing.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # Load "synlik" object
data(ricker_sl)
plot(ricker_sl)
# MCMC estimation
set.seed(4235)
ricker_sl <- smcmc(ricker_sl,
initPar = c(3.2, -1, 2.6),
niter = 50,
burn = 3,
priorFun = function(input, ...) 1,
propCov = diag( c(0.1, 0.1, 0.1) )^2,
nsim = 200,
multicore = FALSE)
# Continue with additional 50 iterations
ricker_sl <- continue(ricker_sl, niter = 50)
plot(ricker_sl)
|
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