mcmc.list-methods: Methods for the 'mcmc.list' class

Description

Methods for 'mcmc.list' objects.

Usage

dcsd(object, ...)
# S3 method for mcmc.list
dcsd(object, ...)
# S3 method for mcmc.list
coef(object, ...)
# S3 method for mcmc.list.dc
confint(object, parm, level = 0.95, ...)
# S3 method for mcmc.list
vcov(object, ...)
# S3 method for mcmc.list.dc
vcov(object, invfisher = TRUE, ...)
# S3 method for mcmc.list
quantile(x, ...)

Value

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.

Arguments

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 (TRUE) should be returned instead of the variance-covariance matrix of the joint posterior distribution (FALSE).

Author

Peter Solymos, solymos@ualberta.ca

Examples

Run this code

if (FALSE) {
## 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)
}