Description Usage Arguments Examples
Plot method for the estimation results of the hierarchical (mixed) diffusion model.
1 2 3 4 5 |
x |
est.mixedDiffusion class, created with 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 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | mu <- c(10, 3, 1); Omega = c(1, 0.4, 0.01)
phi <- sapply(1:3, function(i) rnorm(20, mu[i], sqrt(Omega[i])))
model <- set.to.class("mixedDiffusion", b.fun = function(phi, t, y) phi[1]-phi[2]*y,
parameter = list(mu = mu, Omega = Omega, phi = phi, gamma2 = 0.1),
y0 = function(phi, t) phi[3], sT.fun = function(t, x) sqrt(abs(x)))
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, 6), TRUE))
plot(est, style = "int.phi", phi = phi, par2plot = c(TRUE, FALSE, FALSE))
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.