Description Usage Arguments Details Value Author(s) See Also
View source: R/as.initial.values.R
This function returns the most recent posterior samples from an object
of class demonoid
or demonoid.hpc
, the posterior means
of an object of class iterquad
, the posterior modes of an
object of class laplace
or vb
, the posterior means of an
object of class pmc
with one mixture component, or the latest
means of the importance sampling distribution of an object of class
pmc
with multiple mixture components. The returned values are
intended to be the initial values for future updates.
1 |
x |
This is an object of class |
Unless it is known beforehand how many iterations are required for
IterativeQuadrature
, LaplaceApproximation
,
or VariationalBayes
to converge, MCMC in
LaplacesDemon
to appear converged, or the normalized
perplexity to stabilize in PMC
, multiple updates are
necessary. An additional update, however, should not begin with the
same initial values as the original update, because it will have to
repeat the work already accomplished. For this reason, the
as.initial.values
function may be used at the end of an update
to change the previous initial values to the latest values.
When using LaplacesDemon.hpc
, as.initial.values
should be used when the output is of class demonoid.hpc
, before
the Combine
function is used to combine the multiple
chains for use with Consort
and other functions, because
the Combine
function returns an object of class
demonoid
, and the number of chains will become unknown. The
Consort
function may suggest using
as.initial.values
, but when applied to an object of class
demonoid
, it will return the latest values as if there were
only one chain.
The returned value is a vector (or matrix in the case of an object of
class demonoid.hpc
, or pmc
with multiple mixture
components) of the latest values, which may now be used as initial
values for a future update.
Statisticat, LLC. software@bayesian-inference.com
Combine
,
IterativeQuadrature
,
LaplaceApproximation
,
LaplacesDemon
,
LaplacesDemon.hpc
,
PMC
, and
VariationalBayes
.
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