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

Description Usage Arguments Examples

Description

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

Usage

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## 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, ...)

Arguments

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

Examples

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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 = "")

SimoneHermann/BaPreStoPro documentation built on May 10, 2017, 1:42 p.m.