# plot-est.hiddenDiffusion-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 hidden diffusion model.

## Usage

 ```1 2 3 4``` ```## S4 method for signature 'est.hiddenDiffusion' 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.hiddenDiffusion class, created with method `estimate,hiddenDiffusion-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, γ^2, σ^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 `...` optional plot parameters

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```model <- set.to.class("hiddenDiffusion", b.fun = function(phi, t, y) phi[1]-phi[2]*y, parameter = list(phi = c(10, 1), gamma2 = 1, sigma2 = 0.1), y0 = function(phi, t) 0.5) data <- simulate(model, t = seq(0, 1, by = 0.01), plot.series = TRUE) est <- estimate(model, t = seq(0, 1, by = 0.01), data\$Y, 100) # nMCMC small for example plot(est) plot(est, par2plot = c(rep(FALSE, 3), TRUE, FALSE), ylim = c(0.001, 0.1), par.options = list()) plot(est, burnIn = 10, thinning = 2, reduced = TRUE) plot(est, par.options = list(mar = c(5, 4.5, 4, 2) + 0.1, mfrow = c(3,1)), xlab = "iteration") plot(est, style = "acf", main = "", par2plot = c(TRUE, TRUE, FALSE, FALSE)) 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(FALSE, FALSE, TRUE, TRUE)) ```

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