zoib (version 1.1)

BiRepeated: Data from a correlated bivariate beta distribution with repeated measures

Description

A simulated data set with two correlated Beta variables y1 and y2 on 100 independent cases. Both y1 and y2 are repeatedly measured at a given set of covariate values x = (0, 0.1, 0.2, 0.3, 0.4, 0.5). Joint zoib modeling is applied to examine the effect of x on y1 and y2.

Usage

data(BiRepeated)

References

Liu, F. and Kong, Y. (2015). ZOIB: an R Package for Bayesian Inferences in Beta and Zero One Inflated Beta Regression Models, submitted Liu, F. and Li, Q. (2014) A Bayesian Model for Joint Analysis of Multivariate Repeated Measures and Time to Event Data in Crossover Trials, Statistical Methods in Medical Research, doi: 10.1177/0962280213519594

Examples

library(zoib)
  data("BiRepeated", package = "zoib")
  eg2 <- zoib(y1|y2 ~ x|1|x, data= BiRepeated,  
              random=1, EUID= BiRepeated$id, joint=TRUE,
              zero.inflation = FALSE, one.inflation = FALSE,  			
              prior.Sigma = "UN.unif",n.iter=5000, n.thin=20, n.burn=1000)
  coeff <- eg2$coeff
  traceplot(coeff); 
  autocorr.plot(coeff); 
  check.psrf(coeff)
  summary(coeff)

  n<- nrow(BiRepeated)
  K<- length(BiRepeated$x)
  post.mean1<- apply(rbind(eg2$ypred[[1]][,1:n],eg2$ypred[[2]][,1:n]),2,mean)
  post.mean2<- apply(rbind(eg2$ypred[[1]][,(n+1):(2*n)], eg2$ypred[[2]][,(n+1):(2*n)]),2,mean) 
  
  library(modeest)
  for(i in 1:(n*K) 
    post.mode1[i] <- mlv(rbind(eg2$ypred[[1]][,i],eg2$ypred[[2]][,i]),method = "hsm")$M 
  for(i in (n*K+1):(2*n*K)) 
    post.mode2[i-n*K+1] <- mlv(rbind(eg2$ypred[[1]][,i],eg2$ypred[[2]][,i]),method = "hsm")$M 
    
  plot(BiRepeated$y1, post.mean1, xlim=c(0,1),ylim=c(0,1), col='green2')
  points(BiRepeated$y2,post.mean2,col='purple')
  points(BiRepeated$y2,post.mode1,col='purple',pch=2)
  points(BiRepeated$y2,post.mode2,col='purple',pch=2)
  abline(0,1,col='red')