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

In eta21/DiffusionRgqd: Inference and Analysis for Generalized Quadratic Diffusions

Description

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

Usage

 GQD.estimates(x, thin = 100, burns, CI = c(0.05, 0.95), corrmat =
          FALSE, acf.plot = TRUE, palette = 'mono')

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.
CI	Credibility interval quantiles (for MCMC chains).
corrmat	If TRUE, an estimated correlation matrix is returned in addition to the estimate vector.
acf.plot	If TRUE, an acf plot is drawn for each element of the parameter chain.
palette	Colour palette for drawing trace plots. Default palette = 'mono', otherwise a qualitative palette will be used.

Value

Data frame with parameter estimates and appropriate interval statistics.

Author(s)

Etienne A.D. Pienaar: [e-mail]

References

Updates available on GitHub at https://github.com/eta21.

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)
#===============================================================================

eta21/DiffusionRgqd documentation built on May 16, 2019, 8:53 a.m.