R/Bysmxmss.R

In longit: High Dimensional Longitudinal Data Analysis Using MCMC

#' Bayesian mixed model with random intercepts and random slopes for high dimensional longitudinal data with batch size.
#'
#'
#' Bayesian mixed effect model with random intercept and slopes. Data longitudinally measured missing value and having batched
#' information.
#' Fits using MCMC on longitudinal data set
#' @param m Starting number of column from where repeated observations begin
#' @param tmax Maximum batch of visits considered as repeated measurements
#' @param timepoints Timepoint information on which repeadted observations were taken
#' @param group A categorical variable either 0 or 1. i.e. Gender - 1 male and 0 female
#' @param chains Number of MCMC chains to be performed
#' @param iter Number of iterations to be performed
#' @param data High dimensional longitudinal data
#' @return Gives posterior means, standard deviation.
#' @import R2jags
#' @export
#' @author Atanu Bhattacharjee and Akash Pawar
#' @examples
#' ##
#' data(repdat)
#' @references Bhattacharjee, A. (2020). Bayesian Approaches in Oncology Using R and OpenBUGS. CRC Press.
#'
#' Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. CRC press.
#'
#' Fitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2012). Applied longitudinal analysis (Vol. 998). John Wiley & Sons.
Bysmxmss<-function(m,tmax,timepoints,group,chains,iter,data){
gendergrp<-group
    w1<-m[1]
    w2<-(m[length(m)])+tmax-1
    data1<-data[,w1:w2]
    data2<-data[,1:(w1-1)]

    data1.imp <- missForest(data1) #imputed tmc longit
    data1 <- data.frame(data1.imp$ximp)
    data <- data.frame(data2,data1)
    var1 <- data[,timepoints] #grouping variable on which observation were taken
    dmat <- matrix(nrow=0,ncol=2)

    rlt=list()
    for( k in m){
      m1=k
      m2=k+tmax-1
      Distance = as.matrix(data[,m1:m2])
      colnames(Distance)<-NULL
      mn=length(m1:m2)
      var1 <- var1[1:mn]
      var2 <- data[,gendergrp]
      mdata <- list(R=matrix(c(.001,0,0,.001),2,2), mu0=c(0,0)  ,
                    a2=var2,a1 = var1,
                    Distance = Distance,N=nrow(data),MN=mn)


      mixedmodinslp <- function(){
        for( i in 1:N){
          for(j in 1:MN){
            Distance[ i , j ] ~ dnorm(m[ i , j ], tau)
            m[ i , j  ]  <- (beta[1] + b[ i , 1 ]) + (beta[2] + b[ i , 2 ])*a1[ j ]  + beta[3]*a2[ i ]
          }
        }
        for( i in 1:N){
          b[ i , 1:2 ] ~ dmnorm(mu0[1:2], prec[1:2,1:2])
        }
        prec[1:2,1:2] ~ dwish(R[ 1:2 , 1:2 ],  2)
        for(i in 1:3){   beta[ i ] ~ dnorm(0,0.0001)  }
        tau ~dgamma(0.0001,0.0001)
        sigma <- sqrt(1/(tau*tau))
      }
      inits<-function(){list(beta =c(0,0,0), tau=1, prec=matrix(c(.001,0,0,.001),2,2))}

      param = c("tau","sigma")
      v1.sim.rem1 <- jags(mdata, inits, model.file = mixedmodinslp,
                          parameters.to.save = param,
                          n.chains = chains, n.iter = iter, n.burnin = iter/2 )

    return(v1.sim.rem1)
  }
}

utils::globalVariables(c("b","tau","Male"))