bict.fit: Bayesian Analysis of Incomplete Contingency Tables
In conting: Bayesian Analysis of Contingency Tables

Description Usage Arguments Value Note Author(s) References See Also Examples

This function is the workhorse behind bict and bictu.

1 2	bict.fit(priornum, missing1, missing2, maximal.mod, IP, eta.hat, ini.index, ini.beta, ini.sig, ini.y0, iters, save, name, null.move.prob, a, b, progress)

`priornum`	A numeric scalar indicating which prior is to be used: 1 = `"UIP"`, 2 = `"SBH"`.
`missing1`	A vector of the same length as the number of missing cell counts giving the row numbers of the data.frame in `data` which correspond to the missing cell counts.
`missing2`	A vector of the same length as the number of censored cell counts giving the row numbers of the data.frame in `data` which correspond to the censored cell counts.
`maximal.mod`	An object of class `"glm"` giving the fit of the maximal model.
`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.
`ini.index`	A binary vector, of the same length as the number of log-linear parameters in the maximal model, indicating which parameters are present in the initial model.
`ini.beta`	A numeric vector giving the starting values of the log-linear parameters for the MCMC algorithm.
`ini.sig`	A numeric scalar giving the starting value of sigma^2 for the MCMC algorithm.
`ini.y0`	A numeric vector giving the starting values of the missing and censored cell entries for the MCMC algorithm.
`iters`	The number of iterations of the MCMC algorithm to peform.
`save`	If positive, the function will save the MCMC output to external text files every `save` iterations. If zero , the function will not save the MCMC output to external files.
`name`	A prefix to the external files saved if the argument `save` is positive. If `NULL`, then the external files will have no prefix.
`null.move.prob`	A scalar argument giving the probability of performing a null move, i.e. proposing a move to the current model.
`a`	The shape hyperparameter of the Sabanes-Bove & Held prior, see Overstall & King (2014).
`b`	The scale hyperparameter of the Sabanes-Bove & Held prior, see Overstall & King (2014).
`progress`	Logical argument. If `TRUE`, then a progress bar will be displayed.

The function will return a list with the following components.

`BETA`	An `iters` by p matrix containing the sampled values of the log-linear parameters, where p is the number of log-linear parameters in the maximal model. For elements of this matrix which correspond to a log-linear parameter which is not present for the current model a zero is returned.
`MODEL`	A vector of length `iters` giving the sampled model indicators in hexadecimal format.
`SIG`	A vector of length `iters` giving the sampled values for sigma^2 under the Sabanes-Bove & Held prior. If the unit information prior is used then the components of this vector will be one.
`Y0`	An `iters` by k matrix giving the sampled values of the missing and censored cell counts, where k is the total number of missing and censored cell counts.
`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.

This function will not typically be called by the user.

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/

bict, bictu.

data(spina)
## Load spina data.

spina$z<-spina$y
spina$z[is.na(spina$y)]<-0
## Define a new variable in spina data.frame which is equal to y except where
## y is NA, in which case z=0. This is just so we can fit maximal model to the
## complete contingency table.

maximal.mod<-glm(formula=z~(S1+S2+S3+eth)^2,data=spina,x=TRUE,y=TRUE,
contrasts=list(S1="contr.sum",S2="contr.sum",S3="contr.sum",
eth="contr.sum"))
## Fit maximal model to complete contingency table.

curr.index<-formula2index(big.X=maximal.mod$x,formula=z~S1+S2+S3+eth,data=spina)
## Set up binary vector for independence model.

IP<-t(maximal.mod$x)%*%maximal.mod$x/length(maximal.mod$y)
IP[,1]<-0
IP[1,]<-0
## Set up the inverse scale matrix for the prior distribution under
## the maximal model. 

bmod<-beta_mode(X=maximal.mod$x[!is.na(spina$y),],prior="UIP",
y=maximal.mod$y[!is.na(spina$y)],IP=IP)
## Find the posterior mode under the maximal model fitted to observed cell counts.
eta.hat<-as.vector(maximal.mod$x%*%bmod)
## Find the posterior mode of the linear predictor
## under the maximal model.

set.seed(1)
## Set seed for reproducibility
test1<-bict.fit(priornum=1, missing1=(1:length(maximal.mod$y))[is.na(spina$y)],
missing2=NULL,maximal.mod=maximal.mod, IP=IP, eta.hat=eta.hat, ini.index=curr.index, 
ini.beta=bmod[curr.index==1], ini.sig=1, ini.y0=c(500,200,20),iters=10, save=0, name=NULL,
null.move.prob=0.5, a=0.001, b=0.001, progress = FALSE) 
## Run for 10 iterations starting at model defined by curr.index.
test1$MODEL
## Look at sampled model indicators. Should be:
#  [1] "7e00" "7e00" "7e00" "7e00" "7e00" "7e00" "7e00" "7e00" "7f00" "7f00"

model2index(test1$MODEL,dig=15)
## Convert these to binary indicators of the log-linear parameters.
## Will get: 

#     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
#7e00    1    1    1    1    1    1    0    0    0     0     0
#7e00    1    1    1    1    1    1    0    0    0     0     0
#7e00    1    1    1    1    1    1    0    0    0     0     0
#7e00    1    1    1    1    1    1    0    0    0     0     0
#7e00    1    1    1    1    1    1    0    0    0     0     0
#7e00    1    1    1    1    1    1    0    0    0     0     0
#7e00    1    1    1    1    1    1    0    0    0     0     0
#7e00    1    1    1    1    1    1    0    0    0     0     0
#7f00    1    1    1    1    1    1    1    0    0     0     0
#7f00    1    1    1    1    1    1    1    0    0     0     0
#     [,12] [,13] [,14] [,15]
#7e00     0     0     0     0
#7e00     0     0     0     0
#7e00     0     0     0     0
#7e00     0     0     0     0
#7e00     0     0     0     0
#7e00     0     0     0     0
#7e00     0     0     0     0
#7e00     0     0     0     0
#7f00     0     0     0     0
#7f00     0     0     0     0