Description Usage Arguments Details Value Author(s) References See Also Examples
This function will compute the Bayesian (or posterior predictive) p-value. This can be used as a diagnostic tool to check model adequacy. Additionally this function outputs predictions from the model which can also be used in other assessments of model adequacy.
1 | bayespval(object, n.burnin = 0, thin = 1, statistic = "X2")
|
object |
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 |
statistic |
An optional argument giving the discrepancy statistic to use for calculating the Bayesian p-value. It can be one of
|
See Gelman et al (2004, Chapter 6) for more details on Bayesian p-values and see Overstall & King (2014), and references therein, for details of their application to contingency tables.
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 will produce an object of class "pval"
which is a list
with the following components.
PRED |
An ( |
Tpred |
A vector of length ( |
Tobs |
A vector of length ( |
pval |
A scalar giving the Bayesian p-value, i.e. the proportion of |
statnum |
A numeric scalar identifying which statistic is used. |
statistic |
A character string identifying which statistic is used. |
thin |
The value of the argument |
Antony M. Overstall A.M.Overstall@soton.ac.uk.
Gelman, A., Carlin, J.B., Stern, H.S. & Rubin, D.B. (2004) Bayesian Data Analysis, 2nd edition, Chapman & Hall.
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/
bict
,
bcct
,
print.pval
.
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 | set.seed(1)
## Set seed for reproducibility
data(spina)
## Load spina data
test1<-bict(formula=y~(S1+S2+S3+eth)^2,data=spina,n.sample=50,prior="UIP")
## Do 50 iterations starting at maximal model containing all two-way interactions.
test1p<-bayespval(object=test1,statistic="FreemanTukey",n.burnin=5)
## Use the Freeman-Tukey statistic and a burn-in phase of 5 iterations.
test1p
## Will get following output
#Under the Freeman-Tukey statistic
#
#Summary statistics for T_pred
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 2.812 4.695 5.190 5.777 6.405 14.490
#
#Summary statistics for T_obs
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 4.566 4.861 5.197 5.430 6.108 6.460
#
#Bayesian p-value = 0.4667
## Can do a plot
## Not run: plot(test1p)
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