plot,est.NHPP-method {BaPreStoPro} | R Documentation |
Plot method for the estimation results of the NHPP.
## S4 method for signature 'est.NHPP' plot(x, par.options, style = c("chains", "acf", "density"), par2plot, reduced = FALSE, thinning, burnIn, priorMeans = TRUE, col.priorMean = 2, lty.priorMean = 1, ...)
x |
est.NHPP 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, 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 |
model <- set.to.class("NHPP", parameter = list(xi = c(5, 1/2)), 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$Times, 10000) # nMCMC small for example plot(est) plot(est, burnIn = 1000, thinning = 20, reduced = TRUE) plot(est, xlab = "iteration") plot(est, style = "acf", main = "", par2plot = c(TRUE, FALSE), par.options = list(mfrow = c(1, 1))) 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(FALSE, TRUE), main = "")