knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
To use waspr
the user first needs to load the package as follows:
library(waspr)
The user must provide a 3-dimensional array with posterior samples for all
parameters for each subset posterior (rows = subset posteriors, columns =
parameters, slices = samples). The amount of parameters and samples must be
equal for each subset posterior, these posterior samples may be obtained from
any type of MCMC algorithm. waspr
provides an example array with posterior
samples for 8 parameters for 8 subset posteriors, pois_logistic
, that will be
used to illustrate the functionality of the package.
The main function wasp()
runs the swapping algorithm, combines its output and
computes the Wasserstein barycenter. It has four arguments, mcmc
, that
specifies the 3-dimensional array with samples for each subset posterior, and
optional arguments par.names
, that can be used to specify parameter names,
iter
to specify the maximum number of iterations of the swapping algorithm,
acc
to specify the accuracy of the swapping algorithm and out
to indicate
whether the results per iteration of the swap algorithm should be printed.
out <- wasp(pois_logistic, iter = 10, acc = 0.001, par.names = c("beta_s", "alpha_l", "beta_l", "baseline_sigma", "baseline_mu", "correlation", "sigma_s", "sigma_l"))
wasp()
prints the iteration number and cost function value of the swapping
algorithm. The out
object is of class wasp
and contains several elements. To
obtain the Wasserstein barycenter of the subset posteriors a user can specify
out$barycenter
. This returns a matrix of posterior samples (rows) for all
parameters (columns) of the full data posterior. A summary of the approximation
of the full data posterior is available through summary(out)
.
summary(out)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.