Description Usage Arguments Details Value Author(s) References See Also Examples
GQD.aic()
summarizes the MCMC output from a list of GQD.mle()
objects. This may be used to neatly summarize the MCMC output of various models fitted to a given dataset.
1 |
model.list |
A list of |
type |
Shoould output be of row ( |
GQD.aic()
summarizes the output from various models fitted via GQD.mle()
. By ranking them according to DIC. [=]
indicates which model has the minimal DIC.
Convergence | p | min.likelihood | AIC | BIC | N |
|
Model 1 | 0 | 5 | 171.5576 | [=] 353.1152 | [=] 369.6317 | 201 |
Model 2 | 0 | 5 | 185.7518 | 381.5036 | 398.0201 | 201 |
|
A data frame with summary of model output. See Details. |
Etienne A.D. Pienaar: etiannead@gmail.com
Updates available on GitHub at https://github.com/eta21.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | #===============================================================================
# Simulate a time inhomogeneous diffusion.
#-------------------------------------------------------------------------------
data(SDEsim1)
attach(SDEsim1)
par(mfrow=c(1,1))
expr1=expression(dX[t]==2*(5+3*sin(0.5*pi*t)-X[t])*dt+0.5*sqrt(X[t])*dW[t])
plot(Xt~time,type='l',col='blue',xlab='Time (t)',ylab=expression(X[t]),main=expr1)
#------------------------------------------------------------------------------
# Define coefficients of the process.
#------------------------------------------------------------------------------
GQD.remove()
G0 <- function(t){theta[1]*(theta[2]+theta[3]*sin(0.25*pi*t))}
G1 <- function(t){-theta[1]}
Q0 <- function(t){theta[4]*theta[4]}
theta.start <- c(1,1,1,1) # Starting values for the chain
mesh.points <- 10 # Number of mesh points
m1 <- GQD.mle(Xt,time,mesh=mesh.points,theta=theta.start)
GQD.remove()
G1 <- function(t){theta[1]*(theta[2]+theta[3]*sin(0.25*pi*t))}
G2 <- function(t){-theta[1]}
Q2 <- function(t){theta[4]*theta[4]}
theta.start <- c(1,1,1,1) # Starting values for the chain
mesh.points <- 10 # Number of mesh points
m2 <- GQD.mle(Xt,time,mesh=mesh.points,theta=theta.start)
# Check estimates:
GQD.estimates(m1)
GQD.estimates(m2)
# Compare models:
GQD.aic(list(m1,m2))
#===============================================================================
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