BaPreStoPro (version 0.1)

plot,est.hiddenmixedDiffusion-method: Plot method for the Bayesian estimation results

Description

Plot method for the estimation results of the hidden hierarchical diffusion model.

Usage

"plot"(x, par.options, style = c("chains", "acf", "density", "int.phi"), par2plot, reduced = FALSE, thinning, burnIn, priorMeans = TRUE, col.priorMean = 2, lty.priorMean = 1, level = 0.05, phi, ...)

Arguments

x

est.hiddenmixedDiffusion class, created with method estimate,hiddenmixedDiffusion-method

par.options

list of options for function par()

style

one out of "chains", "acf", "density", "int.phi"

par2plot

logical vector, which parameters to be plotted, order: $(\mu, \Omega, \gamma^2, \sigma^2, Y)$

reduced

logical (1), if TRUE, the chains are thinned and burn-in phase is dropped

thinning

thinning rate, if missing, the proposed one by the estimation procedure is taken

burnIn

burn-in phase, if missing, the proposed one by the estimation procedure is taken

priorMeans

logical(1), if TRUE (default), prior means are marked with a line

col.priorMean

color of the prior mean line, default 2

lty.priorMean

linetype of the prior mean line, default 1

level

level for style = "int.phi"

phi

in the case of simulation study: known values for phi

...

optional plot parameters

Examples

## Not run: 
# mu <- c(10, 3, 1); Omega = c(1, 0.4, 0.01)
# phi <- sapply(1:3, function(i) rnorm(20, mu[i], sqrt(Omega[i])))
# model <- set.to.class("hiddenmixedDiffusion", b.fun = function(phi, t, y) phi[1]-phi[2]*y,
#     parameter = list(mu = mu, Omega = Omega, phi = phi, gamma2 = 1, sigma2 = 0.1),
#     y0 = function(phi, t) phi[3])
# data <- simulate(model, t = seq(0, 1, by = 0.02), plot.series = TRUE)
# est <- estimate(model, t = seq(0, 1, by = 0.02), data$Z, 1000)
# plot(est, burnIn = 10, thinning = 2, reduced = TRUE)
# plot(est, par.options = list(mar = c(5, 4.5, 4, 2) + 0.1, mfrow = c(2,1)), xlab = "iteration")
# plot(est, style = "acf", main = "", par2plot = c(TRUE, TRUE, rep(FALSE, 7)))
# plot(est, style = "density", lwd = 2, priorMean = FALSE,
#    par2plot = c(rep(FALSE, 6), TRUE, TRUE, FALSE))
# plot(est, style = "density", col.priorMean = 1, lty.priorMean = 2, main = "posterior")
# plot(est, style = "acf", par.options = list(), main = "", par2plot = c(rep(FALSE, 6), TRUE, TRUE))
# plot(est, style = "int.phi", phi = phi, par2plot = c(TRUE, FALSE, FALSE))
# ## End(Not run)