mcmcsae (version 0.6.0)

MCMC-object-conversion: Convert a draws component object to another format

Description

Use to_mcmc to convert a draws component to class mcmc.list, allowing one to use MCMC diagnostic functions provided by package coda. Use as.array to convert to an array of dimension (draws, chains, parameters). The array format is supported by some packages for analysis or visualisation of MCMC simulation results, e.g. bayesplot. Use as.matrix to convert to a matrix, concatenating the chains.

Usage

to_mcmc(x)
# S3 method for dc
as.array(x, ...)
# S3 method for dc
as.matrix(x, colnames = TRUE, ...)

Arguments

x

a component of a draws object corresponding to a scalar or vector model parameter.

...

currently ignored.

colnames

whether column names should be set.

Value

The draws component coerced to a mcmc.list, an array, or a matrix.

Examples

# NOT RUN {
data(iris)
sampler <- create_sampler(Sepal.Length ~ reg(~ Petal.Length + Species, name="beta"), data=iris)
sim <- MCMCsim(sampler, burnin=100, n.chain=2, n.iter=400)
summary(sim)
if (require("coda", quietly=TRUE)) {
  mcbeta <- to_mcmc(sim$beta)
  geweke.diag(mcbeta)
}
str(as.array(sim$beta))
str(as.matrix(sim$beta))

# generate some example data
n <- 250
dat <- data.frame(x=runif(n), f=as.factor(sample(1:5, n, replace=TRUE)))
gd <- generate_data(~ reg(~ x + f, Q0=1, name="beta"), data=dat)
dat$y <- gd$y
sampler <- create_sampler(y ~ reg(~ x + f, name="beta"), data=dat)
sim <- MCMCsim(sampler, n.chain=2, n.iter=400)
str(sim$beta)
str(as.array(sim$beta))
bayesplot::mcmc_hist(as.array(sim$beta))
bayesplot::mcmc_dens_overlay(as.array(sim$beta))
# fake data simulation check:
bayesplot::mcmc_recover_intervals(as.array(sim$beta), gd$pars$beta)
bayesplot::mcmc_recover_hist(as.array(sim$beta), gd$pars$beta)

ex <- mcmcsae_example()
plot(ex$dat$fT, ex$dat$y)
sampler <- create_sampler(ex$model, data=ex$dat)
sim <- MCMCsim(sampler, n.chain=2, n.iter=400, store.all=TRUE)
str(sim$beta)
str(as.matrix(sim$beta))
# fake data simulation check:
bayesplot::mcmc_recover_intervals(as.matrix(sim$beta), ex$pars$beta)
bayesplot::mcmc_recover_intervals(as.matrix(sim$u), ex$pars$u)
# }
# NOT RUN {
# }