Methods for the 'mcmc.list' class

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Description

Methods for 'mcmc.list' objects.

Usage

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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, ...)

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).

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.

Note

Some functions only available for the 'mcmc.list.dc' class which inherits from class 'mcmc.list'.

Author(s)

Peter Solymos, solymos@ualberta.ca

See Also

jags.fit, bugs.fit

Examples

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## 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)

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