| mcmc.list-methods | R Documentation |
Methods for 'mcmc.list' objects.
dcsd(object, ...) ## S3 method for class 'mcmc.list' dcsd(object, ...) ## S3 method for class 'mcmc.list' coef(object, ...) ## S3 method for class 'mcmc.list.dc' confint(object, parm, level = 0.95, ...) ## S3 method for class 'mcmc.list' vcov(object, ...) ## S3 method for class 'mcmc.list.dc' vcov(object, invfisher = TRUE, ...) ## S3 method for class 'mcmc.list' quantile(x, ...)
x, object |
MCMC object to be processed. |
parm |
A specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. |
level |
The confidence level required. |
... |
Further arguments passed to functions. |
invfisher |
Logical, if the inverse of the Fisher information matrix
( |
dcsd returns the data cloning standard errors of a posterior
MCMC chain calculated as standard deviation times the square root
of the number of clones.
The coef method returns mean of the posterior MCMC chains
for the monitored parameters.
The confint method returns Wald-type confidence intervals
for the parameters assuming asymptotic normality.
The vcov method returns the inverse of the Fisher
information matrix (invfisher = TRUE) or the covariance matrix
of the joint posterior distribution (invfisher = FALSE).
The invfisher is valid only for mcmc.list.dc
(data cloned) objects.
The quantile method returns quantiles for each variable.
Some functions only available for the 'mcmc.list.dc' class which inherits from class 'mcmc.list'.
Peter Solymos, solymos@ualberta.ca
jags.fit, bugs.fit
## Not run:
## simple regression example from the JAGS manual
jfun <- function() {
for (i in 1:N) {
Y[i] ~ dnorm(mu[i], tau)
mu[i] <- alpha + beta * (x[i] - x.bar)
}
x.bar <- mean(x)
alpha ~ dnorm(0.0, 1.0E-4)
beta ~ dnorm(0.0, 1.0E-4)
sigma <- 1.0/sqrt(tau)
tau ~ dgamma(1.0E-3, 1.0E-3)
}
## data generation
set.seed(1234)
N <- 100
alpha <- 1
beta <- -1
sigma <- 0.5
x <- runif(N)
linpred <- crossprod(t(model.matrix(~x)), c(alpha, beta))
Y <- rnorm(N, mean = linpred, sd = sigma)
## data for the model
dcdata <- dclone(list(N = N, Y = Y, x = x), 5, multiply = "N")
## data cloning
dcmod <- jags.fit(dcdata, c("alpha", "beta", "sigma"), jfun,
n.chains = 3)
summary(dcmod)
coef(dcmod)
dcsd(dcmod)
confint(dcmod)
vcov(dcmod)
vcov(dcmod, invfisher = FALSE)
quantile(dcmod)
## End(Not run)