Description Usage Arguments Details Value Note Author(s) References See Also
These functions produce summaries of objects of class "bcct"
and "bict"
. They also control
how these summaries are printed.
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## S3 method for class 'bcct'
summary(object, n.burnin = 0, thin = 1, cutoff = 0.75, statistic = "X2",
best = NULL, scale = 0.1, prob.level = 0.95, ...)
## S3 method for class 'sbcct'
print(x, ..., digits = max(3, getOption("digits") - 3))
## S3 method for class 'bict'
summary(object, n.burnin = 0, thin = 1, cutoff = 0.75, statistic = "X2",
best = NULL, scale = 0.1, prob.level = 0.95, ...)
## S3 method for class 'sbict'
print(x, ..., digits = max(3, getOption("digits") - 3))
|
object |
An object of class |
x |
An object of class |
n.burnin |
An optional argument giving the number of iterations to use as burn-in. The default value is 0. |
thin |
An optional argument giving the amount of thinning to use, i.e. the computations are
based on every |
cutoff |
An optional argument giving the cutoff posterior probability for displaying posterior
summary statistics of the log-linear parameters. Only those log-linear parameters with
a posterior probability greater than |
statistic |
An optional argument giving the discrepancy statistic to use for calculating the Bayesian p-value. It can be one of
|
best |
An optional argument for controlling how the posterior model probabilities are returned
as output. The function will return details on the |
scale |
An optional argument for controlling how the posterior model probabilities are returned
as output. The function will return details on the models with the posterior model probability
larger than |
prob.level |
An optional argument giving the probability content of the highest posterior density intervals (HPDIs). The default value is 0.95. |
digits |
An optional argument controling the rounding of output. |
... |
Arguments to be passed to and from other methods. |
The functions summary.bcct
and summary.bict
rely on the functions
inter_stats
, mod_probs
, bayespval
, and (in the case
of summary.bict
) total_pop
. For extra information about the output from these
functions, see the associated help files.
The use of thinning is recommended when the number of MCMC iterations and/or the number of log-linear parameters in the maximal model are/is large, which may cause problems with comuter memory storage.
The function summary.bcct
will return an object of class "sbcct"
which is a list
with the following components.
BETA |
An |
MODEL |
A vector of length |
SIG |
A vector of length |
rj_acc |
A binary vector of the same length as the number of reversible jump moves attempted. A 0 indicates that the proposal was rejected, and a 1 that the proposal was accepted. |
mh_acc |
A binary vector of the same length as the number of Metropolis-Hastings moves attempted. A 0 indicates that the proposal was rejected, and a 1 that the proposal was accepted. |
priornum |
A numeric scalar indicating which prior was used: 1 = |
maximal.mod |
An object of class |
IP |
A p by p matrix giving the inverse of the prior scale matrix for the maximal model. |
eta.hat |
A vector of length n (number of cells) giving the posterior mode of the linear predictor under the maximal model. |
save |
The argument |
name |
The argument |
int_stats |
A list which contains the same components as an object of class |
mod_stats |
A list which contains the same components as an object of class |
pval_stats |
A list which contains the same components as an object of class |
The function summary.bict
will return an object of class "sbict"
which is a list
with the same components as an object of class "sbcct"
and the following additional
components.
Y0 |
An |
tpop_stats |
A list which contains the same components as an object of class |
The functions print.sbcct
and print.sbict
will print out the MCMC acceptance rates, posterior
summary statistics for the log-linear parameters, the posterior model probabilities, the Bayesian p-value
and (in the case of print.sbict
) posterior summary statistics for the total population size.
For examples see the help files for bcct
and bict
.
Antony M. Overstall A.M.Overstall@soton.ac.uk.
Overstall, A.M. & King, R. (2014) conting: An R package for Bayesian analysis of complete and incomplete contingency tables. Journal of Statistical Software, 58 (7), 1–27. http://www.jstatsoft.org/v58/i07/
bcct
,
bict
,
accept_rate
,
bayespval
,
inter_stats
,
mod_probs
,
total_pop
.
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