Acts on a gp, gpvec, dgp2,
dgp2vec, dgp3, or dgp3vec object.
Continues MCMC sampling of hyperparameters and hidden layers using
settings from the original object. Appends new samples to existing
samples. When vecchia = TRUE, this function provides the option
to update Vecchia ordering/conditioning sets based on latent layer
warpings through the specification of re_approx = TRUE.
continue(object, new_mcmc, verb, re_approx, ...)# S3 method for gp
continue(object, new_mcmc = 1000, verb = TRUE, ...)
# S3 method for dgp2
continue(object, new_mcmc = 1000, verb = TRUE, ...)
# S3 method for dgp3
continue(object, new_mcmc = 1000, verb = TRUE, ...)
# S3 method for gpvec
continue(object, new_mcmc = 1000, verb = TRUE, re_approx = FALSE, ...)
# S3 method for dgp2vec
continue(object, new_mcmc = 1000, verb = TRUE, re_approx = FALSE, ...)
# S3 method for dgp3vec
continue(object, new_mcmc = 1000, verb = TRUE, re_approx = FALSE, ...)
object of the same class with the new iterations appended
object from fit_one_layer, fit_two_layer, or
fit_three_layer
number of new MCMC iterations to conduct and append
logical indicating whether to print iteration progress
logical indicating whether to re-randomize the ordering
and update Vecchia nearest-neighbor conditioning sets (only for fits
with vecchia = TRUE)
N/A
See fit_one_layer, fit_two_layer, or
fit_three_layer for details on MCMC. The resulting
object will have nmcmc equal to the previous nmcmc plus
new_mcmc. It is recommended to start an MCMC fit then
investigate trace plots to assess burn-in. The primary use of this
function is to gather more MCMC iterations in order to obtain burned-in
samples.
Specifying re_approx = TRUE updates random orderings and
nearest-neighbor conditioning sets (only for vecchia = TRUE
fits). In one-layer, there is no latent warping but the Vecchia
approximation is still re-randomized and nearest-neighbors are adjusted
accordingly. In two- and three-layers, the latest samples of hidden
layers are used to update nearest-neighbors. If you update the
Vecchia approximation, you should later remove previous samples
(updating the approximation effectively starts a new chain). When
re_approx = FALSE the previous orderings and conditioning sets
are used (maintaining the continuity of the previous chain).
# See ?fit_two_layer for an example
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