DiffusionRgqd (version 0.1.1)

GQD.estimates: Extract Parmaeter Estimates from .mle() or .mcmc() Objects.

Description

GQD.estimates() calculates parameter estimates from .mle() or .mcmc() model objects.

Usage

GQD.estimates(x, thin = 100, burns)

Arguments

x

List object of type 'GQD.mcmc' or 'GQD.mle'. That is, when model =GQD.mcmc() then model constitutes an appropriate object for x.

thin

Thinnging level for parameter chain.

burns

Number of MCMC updates to discard before calculating estimates.

Value

• Data frame with parameter estimates and appropriate interval statistics.

References

Updates available on GitHub at https://github.com/eta21. Visit http://etiennead.wix.com/diffusionr for more details on the DiffusionRgqd package.

See Also

GQD.mcmc, GQD.mle, BiGQD.mcmc and BiGQD.mle.

Examples

#===============================================================================
# This example simulates a time inhomogeneous diffusion and shows how to conduct
# inference using GQD.mcmc
#-------------------------------------------------------------------------------
library(DiffusionRgqd)
data(SDEsim1)
par(mfrow=c(1,1))
x <- SDEsim1
plot(x$Xt~x$time,type='l',col='blue')
#------------------------------------------------------------------------------
# Define parameterized coefficients of the process, and set up starting
# parameters.
# True model: dX_t = 2(5+3sin(0.25 pi t)-X_t)dt+0.5sqrt(X_t)dW_t
#------------------------------------------------------------------------------

# Remove any existing coeffients. If none are pressent NAs will be returned, but
# this is a safeguard against overlapping.
GQD.remove()

# Define time dependant coefficients. Note that all functions have a single argument.
# This argument has to be `t' in order for the dependancy to be recognized.
# theta does not have to be defined as an argument.

G0 <- function(t){theta[1]*(theta[2]+theta[3]*sin(0.25*pi*t))}
G1 <- function(t){-theta[1]}
Q1 <- function(t){theta[4]*theta[4]}

theta.start  <- c(1,1,1,1)                    # Starting values for the chain
proposal.sds <- c(0.4,0.3,0.2,0.1)*1/2        # Std devs for proposal distributions
mesh.points  <- 10                            # Number of mesh points
updates      <- 50000                         # Perform 50000 updates

#------------------------------------------------------------------------------
# Run the MCMC procedure for the model defined above
#------------------------------------------------------------------------------

m1 <- GQD.mcmc(x$Xt,x$time,mesh=mesh.points,theta=theta.start,sds=proposal.sds,
               updates=updates)

# Calculate estimates:
GQD.estimates(m1,thin=200)
#===============================================================================