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

## Description

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

## Usage

 ```1 2 3 4``` ```## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```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.