## generate some data
set.seed(111)
n <- 500
## regressors
dat <- data.frame(x = runif(n, -3, 3))
## response
dat$y <- with(dat, 1.5 + sin(x) + rnorm(n, sd = 0.6))
## Not run:
# ## estimate model
# b <- bayesx(y ~ sx(x), data = dat)
# summary(b)
#
# ## plot sampling path for
# ## the variance
# plot(b, term = "sx(x)", which = "var-samples")
#
# ## plot sampling paths for
# ## coefficients
# plot(b, term = "sx(x)", which = "coef-samples")
#
# ## plot maximum autocorrelation of
# ## all sampled parameters of term s(x)
# plot(b, term = "sx(x)", which = "coef-samples", max.acf = TRUE)
#
# ## extract samples of term sx(x)
# sax <- as.matrix(samples(b, term = "sx(x)"))
#
# ## now use plotsamples
# plotsamples(sax, selected = "sx(x)")
#
# ## some variations
# plotsamples(sax, selected = "sx(x)", acf = TRUE)
# plotsamples(sax, selected = "sx(x)", acf = TRUE, lag.max = 200)
# ## End(Not run)
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