MCMC-object-conversion {mcmcsae}

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. Finally, use to_draws_array to convert either a draws component or (a subset of components of) an mcdraws object to a draws_array object as defined in package posterior.

Usage

to_mcmc(x)

to_draws_array(x, components = NULL)

## S3 method for class 'dc'
as.array(x, ...)

## S3 method for class 'dc'
as.matrix(x, colnames = TRUE, ...)

Arguments

x

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

components

optional character vector of names of draws components in an mcdraws object. This can be used to select a subset of components to convert to draws_array format.

...

currently ignored.

colnames

whether column names should be set.

Value

The draws component(s) coerced to an mcmc.list object, a draws_array object, an array, or a matrix.

Examples

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$beta)
  geweke.diag(mcbeta)
}
if (require("posterior", quietly=TRUE)) {
  mcbeta <- to_draws_array(sim$beta)
  mcbeta
  draws <- to_draws_array(sim)
  str(draws)
}
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, prior=pr_normal(precision=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)