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

Plot method for the estimation results of the hierarchical (mixed) regression model.

## S4 method for signature 'est.mixedRegression'
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, ...)

`x`	est.mixedRegression class, created with method `estimate,mixedRegression-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: (μ, Ω, γ^2)
`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

mu <- c(1, 3); Omega = c(0.4, 0.01)
phi <- sapply(1:2, function(i) rnorm(20, mu[i], sqrt(Omega[i])))
model <- set.to.class("mixedRegression", fun = function(phi, t) phi[1]*t + phi[2],
    parameter = list(mu = mu, Omega = Omega, phi = phi, gamma2 = 0.1))
data <- simulate(model, t = seq(0, 1, by = 0.02), plot.series = TRUE)
est <- estimate(model, t = seq(0, 1, by = 0.02), data, 100)  # nMCMC small for example
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 = "")
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(), main = "", par2plot = c(rep(FALSE, 4), TRUE))
plot(est, style = "int.phi", phi = phi, par2plot = c(TRUE, FALSE))