coef.mcmc.list {dclone}R Documentation

Functions and methods for the 'mcmc.list' class

Description

Functions and methods for 'mcmc.list' objects.

Usage

dcsd(x, na.rm = FALSE)
as.mcmc.list.dc(x, ...)
## 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.dc':
vcov(object, ...)
## S3 method for class 'mcmc.list':
quantile(x, ...)

Arguments

x, object MCMC object to be processed.
na.rm Logical, if NAs should be removed.
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.

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 function as.mcmc.list.dc creates a data cloning version of an 'mcmc.list' object
The coef method returns mean of the posterior MCMC chains for the monitored parameters.
The confint method returns confidence intervals for the parameters assuming asymptotic normality.
The vcov method returns the inverse of the Fisher information matrix.
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'. Such statistics for Bayesian models are available after coercion by the function as.mcmc.list.dc, but the frequentis meaning of the statistics will not be applicable.

Author(s)

P\'eter S\'olymos, solymos@ualberta.ca

See Also

jags.fit, bugs.fit

Examples

## 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 <- 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)
quantile(dcmod)
## End(Not run)

[Package dclone version 1.0-0 Index]