1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/aaGenerics.R, R/methods-BFBayesFactor.R,
% R/methods-BFBayesFactorTop.R, R/methods-BFmodelSample.R, R/methods-BFodds.R
\docType{methods}
\name{recompute}
\alias{recompute}
\alias{recompute,BFBayesFactor-method}
\alias{recompute,BFBayesFactorTop-method}
\alias{recompute,BFmcmc-method}
\alias{recompute,BFodds-method}
\title{Recompute a Bayes factor computation or MCMC object.}
\usage{
recompute(
x,
progress = getOption("BFprogress", interactive()),
multicore = FALSE,
callback = function(...) as.integer(0),
...
)
\S4method{recompute}{BFBayesFactor}(
x,
progress = getOption("BFprogress", interactive()),
multicore = FALSE,
callback = function(...) as.integer(0),
...
)
\S4method{recompute}{BFBayesFactorTop}(
x,
progress = getOption("BFprogress", interactive()),
multicore = FALSE,
callback = function(...) as.integer(0),
...
)
\S4method{recompute}{BFmcmc}(
x,
progress = getOption("BFprogress", interactive()),
multicore = FALSE,
callback = function(...) as.integer(0),
...
)
\S4method{recompute}{BFodds}(
x,
progress = getOption("BFprogress", interactive()),
multicore = FALSE,
callback = function(...) as.integer(0),
...
)
}
\arguments{
\item{x}{object to recompute}
\item{progress}{report progress of the computation?}
\item{multicore}{Use multicore, if available}
\item{callback}{callback function for third-party interfaces}
\item{...}{arguments passed to and from related methods}
}
\value{
Returns an object of the same type, after repeating the sampling (perhaps with more iterations)
}
\description{
Take an object and redo the computation (useful for sampling). In cases where sampling is
used to compute the Bayes factor, the estimate of the precision of new samples will be added
to the estimate precision of the old sample will be added to produce a new estimate of the
precision.
}
\examples{
## Sample from the posteriors for two models
data(puzzles)
## Main effects model; result is a BFmcmc object, inheriting
## mcmc from the coda package
bf = lmBF(RT ~ shape + color + ID, data = puzzles, whichRandom = "ID",
progress = FALSE)
## recompute Bayes factor object
recompute(bf, iterations = 1000, progress = FALSE)
## Sample from posterior distribution of model above, and recompute:
chains = posterior(bf, iterations = 1000, progress = FALSE)
newChains = recompute(chains, iterations = 1000, progress=FALSE)
}
|
