plot,est.hiddenDiffusion-method {BaPreStoPro} | R Documentation |
Plot method for the estimation results of the hidden diffusion model.
## 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, ...)
x |
est.hiddenDiffusion class, created with 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 |
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))