Compute Gelman and Rubin's convergence diagnostics from multicore BayesX models.
This function takes a fitted bayesx object estimated with multiple chains/cores and
computes the Gelman and Rubin's convergence diagnostic of the model parameters using function
gelman.diag provided in package coda.
GRstats(object, term = NULL, ...)
object |
an object of class |
term |
character or integer. The term for which the diagnostics should be computed,
see also function |
... |
arguments passed to function |
An object returned from gelman.diag.
Nikolaus Umlauf, Thomas Kneib, Stefan Lang, Achim Zeileis.
## Not run:
## generate some data
set.seed(111)
n <- 500
## regressors
dat <- data.frame(x = runif(n, -3, 3), z = runif(n, -3, 3),
w = runif(n, 0, 6), fac = factor(rep(1:10, n/10)))
## response
dat$y <- with(dat, 1.5 + sin(x) + cos(z) * sin(w) +
c(2.67, 5, 6, 3, 4, 2, 6, 7, 9, 7.5)[fac] + rnorm(n, sd = 0.6))
## estimate model
b <- bayesx(y ~ sx(x) + sx(z, w, bs = "te") + fac,
data = dat, method = "MCMC", chains = 3)
## obtain Gelman and Rubin's convergence diagnostics
GRstats(b, term = c("sx(x)", "sx(z,w)"))
GRstats(b, term = c("linear-samples", "var-samples"))
## of all parameters
GRstats(b, term = c("sx(x)", "sx(z,w)",
"linear-samples", "var-samples"))
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