Special summary statistics of the OpenBUGS output.
monitor(a, n.chains = dim(a)[2], trans = NULL, keep.all = FALSE, Rupper.keep = FALSE)
conv.par(x, n.chains, Rupper.keep = TRUE)
a n * m * k
array: m
sequences of length
n
, k
variables measured
number of Markov chains
a vector of length k
: "" if no transformation, or
"log" or "logit" (If trans
is NULL
, it will be set to
"log" for parameters that are all-positive and 0 otherwise.)
if FALSE
(default), first half of a
will
be discarded
if FALSE
, don't return Rupper
for internal use only
for monitor
:
list of "mean","sd", quantiles
("2.5%","25%","50%","75%","97.5%"), "Rhat" if
n.chains>1
, "Rupper" if (Rupper.keep == TRUE) &&
(n.chains > 1)
, and "n.eff" if n.chains > 1
emipirical quantiles of simulated sequences
estimated potential scale reduction (that would be achieved by continuing simulations forever) has two components: an estimate and an approx. 97.5% upper bound
effective sample size: m*n*min(sigma.hat^2/B,1)
.
This is a crude measure of sample size because it relies on the
between variance, B
, which can only be estimated with m
degrees of freedom.
conv.par
is intended for internal use only.
The main function to be called by the user is bugs
.