plot-est.jumpRegression-method: Plot method for the Bayesian estimation results
In BaPreStoPro: Bayesian Prediction of Stochastic Processes

Plot method for the estimation results of the jump regression model.

## S4 method for signature 'est.jumpRegression'
plot(x, par.options, style = c("chains", "acf",
  "density"), par2plot, reduced = FALSE, thinning, burnIn,
  priorMeans = TRUE, col.priorMean = 2, lty.priorMean = 1, ...)

`x`	est.jumpRegression class, created with method `estimate,jumpRegression-method`
`par.options`	list of options for function par()
`style`	one out of "chains", "acf", "density"
`par2plot`	logical vector, which parameters to be plotted, order: (φ, θ, γ^2, ξ, N)
`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
`...`	optional plot parameters

model <- set.to.class("jumpRegression", fun = function(t, N, theta) exp(theta[1]*t) + theta[2]*N,
  parameter = list(theta = c(2, 2), gamma2 = 0.25, xi = c(3, 0.5)),
  Lambda = function(t, xi) (t/xi[2])^xi[1])
data <- simulate(model, t = seq(0, 1, by = 0.01), plot.series = TRUE)
est <- estimate(model, t = seq(0, 1, by = 0.01), data, 1000)  # nMCMC small for example
plot(est)
plot(est, burnIn = 100, thinning = 2, reduced = TRUE)
plot(est, par.options = list(mar = c(5, 4.5, 4, 2) + 0.1, mfrow = c(2, 3)), xlab = "iteration")
plot(est, style = "acf", main = "", par2plot = c(TRUE, FALSE, FALSE, TRUE, TRUE))
plot(est, style = "density", lwd = 2, priorMean = FALSE)
plot(est, style = "density", col.priorMean = 1, lty.priorMean = 2, main = "posterior")
plot(est, style = "acf", par.options = list(), par2plot = c(TRUE, rep(FALSE, 4)), main = "")