R Under development (unstable) (2025-08-19 r88650 ucrt) -- "Unsuffered Consequences"
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> # Example 2:  bivariate beta with repeated measures
> library(zoib)
Loading required package: rjags
Loading required package: coda
Linked to JAGS 4.3.1
Loaded modules: basemod,bugs
Loading required package: abind
Loading required package: Formula
> data("BiRepeated", package = "zoib")
> d= BiRepeated[1:100,]
> eg2 <- zoib(y1|y2 ~ x|1|x, data= d, n.response=2,
+             random=1, EUID= d$id,
+             zero.inflation = c(FALSE,FALSE), one.inflation = c(FALSE,FALSE),				
+             prior.Sigma = "VC.unif", n.iter=5, n.thin=1, n.burn=1)
[1] "***************************************************************************"
[1] "* List of parameter for which the posterior samples are generated         *"
[1] "* b: regression coeff in the linear predictor for the mean of beta dist'n *"
[1] "* d: regression coeff in the linear predictor for the sum of the two      *"
[1] "*    shape parameters in the beta distribution                            *"
[1] "* b0: regression coeff in the linear predictor for Prob(y=0)              *"
[1] "* b1: regression coeff in the linear predictor for Prob(y=1)              *"
[1] "***************************************************************************"
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 200
   Unobserved stochastic nodes: 48
   Total graph size: 6838

Initializing model

NOTE: Stopping adaptation


[1] "NOTE: in the header of Markov Chain Monte Carlo (MCMC) output of    "
[1] "parameters (coeff), predicted values (ypred), residuals (resid), and"
[1] "standardized residuals (resid.std), *Start, End, Thinning Interval* "
[1] "values are after the initial burning and thinning periods specified "
[1] "by the user. For example, n.iter = 151, n.thin = 2, n.burn=1,       "
[1] "then MCMC header of the *coeff* output would read as follows        "
[1] "--------------------------------------------------------------------"
[1] "Markov Chain Monte Carlo (MCMC) output:"
[1] "Start = 1"
[1] "End = 75"
[1] "Thinning interval = 1"
[1] "--------------------------------------------------------------------"
[1] "                                                                    "
[1] "Coefficients are presented in the order of b, b0 (if zero.inflation=TRUE),"
[1] "b1 (if one.inflation=TRUE), and d. If the names of independent variables X"
[1] "are not shown for the coefficients within each type (b, b0, b1, d), the   "
[1] "first coeffient is always the intercept, followed the coefficients for the"
[1] "X's in the order as how they are entered in the model specification.      "
[1] "--------------------------------------------------------------------------"
> coeff<- eg2$coeff
> ypred<- eg2$ypred
> Xb<- eg2$Xb
> Xd<- eg2$Xd
> Xb0<- eg2$Xb0
> 
> if(0){
+ eg2 <- zoib(y1 ~x|1|x, data= BiRepeated, n.response=1,
+             random=1, EUID= BiRepeated$id, joint=FALSE,
+             zero.inflation = FALSE, one.inflation = FALSE,  			
+             prior.Sigma = "UN.halfcauchy", n.iter=60, n.thin=5, n.burn=10,            
+             inits=list(list(b0=NULL,b1=NULL,b=matrix(c(-1.3,-2.6),2,1),
+                             d=matrix(1.75,1,1),sigma=c(0.16,0.25),R=c(1,0.12,1)),
+                        list(b0=NULL,b1=NULL,b=matrix(c(-0.7,-1.4),2,1),
+                             d=matrix(3.25,1,1),sigma=c(0.25,0.16),R=c(1,0.08,1)))
+ )
+ coeff<- eg2$coeff
+ traceplot(coeff)
+ 
+ 
+ eg2 <- zoib(y1|y2 ~ x|1|x, data= BiRepeated, n.response=2,
+             random=1, EUID= BiRepeated$id,
+             zero.inflation = FALSE, one.inflation = FALSE,  			
+             prior.Sigma = "UN.halfcauchy", n.iter=100, n.thin=2, n.burn=1,
+             inits=list(list(b0=NULL,b1=NULL,b=matrix(c(-1.3,-2.6,0.5,1.0),2,2),
+                             d=matrix(c(1.75,2),1,2),sigma=c(0.16,0.25),R=c(1,0.12,1)),
+                        list(b0=NULL,b1=NULL,b=matrix(c(-0.7,-1.4,0.5,1.0),2,2),
+                             d=matrix(c(3.25,2),1,2),sigma=c(0.25,0.16),R=c(1,0.08,1)))
+ )
+ coeff<- eg2$coeff
+ traceplot(coeff)            
+ }
> 
> proc.time()
   user  system elapsed 
   0.87    0.14    1.00