Continue Sampling from the Posterior of a spifa Model

Runs niter additional MCMC iterations, warm-started from the last posterior draw of object (the easiness/discrimination/residual correlation/predictor-effect/Gaussian process parameters), reusing the data, priors, and constraints of the original fit. Returns only the new draws, not concatenated with object's – see Details.

Usage

# S3 method for class 'spifa'
update(object, niter = 100, thin = 1, burnin = 0, ...)

Arguments

object: A fitted spifa object, as returned by spifa with execute = TRUE.
niter: Number of additional MCMC iterations to run and store.
thin: Thinning interval for the newly stored MCMC samples.
burnin: Number of initial iterations of this continuation to discard before storing (see spifa's burnin).
...: Further arguments (currently unused).

Value

A new spifa object holding only the continuation draws (see Description).

Details

The adaptive Metropolis-Hastings proposal tuning (for the residual correlation and, for spifa/spifa_pred, the Gaussian process parameters) resumes from wherever object's own run left it off, rather than restarting from object's original adaptive settings – so a chain of update() calls keeps refining its proposal instead of re-paying for adaptation each time.

update() always uses the same standardize setting object itself was originally fit with (see spifa); it isn't an argument here. standardize's rescale is still computed independently for each call, from that call's own posterior draws, so object and the object returned here can end up on slightly different absolute scales even though both represent the same continuous chain. Combine them yourself (e.g. rbind() on their as_draws_matrix form) if you want one continuous chain; fit with standardize = FALSE in the first place if you want every continuation on a genuinely identical, unrescaled scale.

Author

Erick A. Chacón-Montalván

Examples

# \donttest{
data(ipixuna)
samples <- spifa(items ~ 1, data = ipixuna, nfactors = 3, ngp = 0, niter = 1000)
more_samples <- update(samples, niter = 1000)
# }