R/BANOVA.NormalNormal.R

In BANOVA: Hierarchical Bayesian ANOVA Models

BANOVA.NormalNormal <-
function(l1_formula = 'NA', 
         l2_formula = 'NA', 
         data, 
         id, 
         l1_hyper, 
         l2_hyper, 
         burnin, 
         sample, 
         thin, 
         adapt, 
         conv_speedup,
         jags){
  cat('Model initializing...\n')
  if (l1_formula == 'NA'){
    stop("Formula in level 1 is missing or not correct!")
  }else{
    mf1 <- model.frame(formula = l1_formula, data = data)
  }
  single_level = F
  if (l2_formula == 'NA'){
    # one level models
    single_level = T
    # check y, if it is numeric
    y <- model.response(mf1)
    if (!inherits(y, 'numeric')){
      warning("The response variable must be numeric (data class also must be 'numeric')")
      y <- as.numeric(y)
      warning("The response variable has been converted to numeric")
    }
    # check each column in the dataframe should have the class 'factor' or 'numeric', no other classes such as 'matrix'...
    for (i in 1:ncol(data)){
      if(!inherits(data[,i], 'factor') && !inherits(data[,i], 'numeric') && !inherits(data[,i], 'integer')) stop("data class must be 'factor', 'numeric' or 'integer'")
      # checking numerical predictors, converted to categorical variables if the number of levels is <= 3
      if ((inherits(data[,i], 'numeric') | inherits(data[,i], 'integer')) & length(unique(data[,i])) <= 3){
        data[,i] <- as.factor(data[,i])
        warning("Variables(levels <= 3) have been converted to factors")
      }
    }
    n <- nrow(data)
    uni_id <- unique(id)
    num_id <- length(uni_id)
    new_id <- rep(0, length(id)) # store the new id from 1,2,3,...
    for (i in 1:length(id))
      new_id[i] <- which(uni_id == id[i])
    id <- new_id
    dMatrice <- design.matrix(l1_formula, l2_formula, data = data, id = id)
    JAGS.model <- JAGSgen.normalNormal(dMatrice$X, dMatrice$Z, l1_hyper, l2_hyper, conv_speedup)
    JAGS.data <- dump.format(list(n = n, y = dMatrice$y, X = dMatrice$X))
    result <- run.jags (model = JAGS.model$sModel, data = JAGS.data, inits = JAGS.model$inits, n.chains = 1,
                        monitor = c(JAGS.model$monitorl1.parameters, JAGS.model$monitorl2.parameters), 
                        burnin = burnin, sample = sample, thin = thin, adapt = adapt, jags = jags, summarise = FALSE, 
                        method="rjags")
    samples <- result$mcmc[[1]]
    # find the correct samples, in case the order of monitors is shuffled by JAGS
    n_p_l1 <- length(JAGS.model$monitorl1.parameters)
    index_l1_param<- array(0,dim = c(n_p_l1,1))
    for (i in 1:n_p_l1)
      index_l1_param[i] <- which(colnames(result$mcmc[[1]]) == JAGS.model$monitorl1.parameters[i])
    if (length(index_l1_param) > 1)
      samples_l1_param <- result$mcmc[[1]][,index_l1_param]
    else
      samples_l1_param <- matrix(result$mcmc[[1]][,index_l1_param], ncol = 1)
    colnames(samples_l1_param) <- colnames(result$mcmc[[1]])[index_l1_param]
    cat('Constructing ANOVA/ANCOVA tables...\n')
    dMatrice$Z <-  array(1, dim = c(1,1), dimnames = list(NULL, ' '))
    attr(dMatrice$Z, 'assign') <- 0
    attr(dMatrice$Z, 'varNames') <- " "
    samples_l2_param <- NULL
    anova.table <- table.ANCOVA(samples_l2_param, dMatrice$Z, dMatrice$X, samples_l1_param, array(y, dim = c(length(y), 1))) # for ancova models
    coef.tables <- table.coefficients(samples_l1_param, JAGS.model$monitorl1.parameters, colnames(dMatrice$Z), colnames(dMatrice$X), 
                                       attr(dMatrice$Z, 'assign') + 1, attr(dMatrice$X, 'assign') + 1)
    pvalue.table <- table.pvalue(coef.tables$coeff_table, coef.tables$row_indices, l1_names = attr(dMatrice$Z, 'varNames'), 
                                  l2_names = attr(dMatrice$X, 'varNames'))
    conv <- conv.geweke.heidel(samples_l1_param, colnames(dMatrice$Z), colnames(dMatrice$X))
    mf2 <- NULL
    class(conv) <- 'conv.diag'
    cat('Done.\n')

  }else{
    mf2 <- model.frame(formula = l2_formula, data = data)
    # check y, if it is numeric
    y <- model.response(mf1)
    if (!inherits(y, 'numeric')){
      warning("The response variable must be numeric (data class also must be 'numeric')")
      y <- as.numeric(y)
      warning("The response variable has been converted to numeric")
    }
    # check each column in the dataframe should have the class 'factor' or 'numeric', no other classes such as 'matrix'...
    for (i in 1:ncol(data)){
      if(!inherits(data[,i], 'factor') && !inherits(data[,i], 'numeric') && !inherits(data[,i], 'integer')) stop("data class must be 'factor', 'numeric' or 'integer'")
      # checking numerical predictors, converted to categorical variables if the number of levels is <= 3
      if ((inherits(data[,i], 'numeric') | !inherits(data[,i], 'integer')) & length(unique(data[,i])) <= 3){
        data[,i] <- as.factor(data[,i])
        warning("Variables(levels <= 3) have been converted to factors")
      }
    }
    
    n <- nrow(data)
    uni_id <- unique(id)
    num_id <- length(uni_id)
    new_id <- rep(0, length(id)) # store the new id from 1,2,3,...
    for (i in 1:length(id))
      new_id[i] <- which(uni_id == id[i])
    id <- new_id
    dMatrice <- design.matrix(l1_formula, l2_formula, data = data, id = id)
    
    JAGS.model <- JAGSgen.normalNormal(dMatrice$X, dMatrice$Z, l1_hyper, l2_hyper, conv_speedup)
    JAGS.data <- dump.format(list(n = n, id = id, M = num_id, y = dMatrice$y, X = dMatrice$X, Z = dMatrice$Z))
    result <- run.jags (model = JAGS.model$sModel, data = JAGS.data, inits = JAGS.model$inits, n.chains = 1,
                        monitor = c(JAGS.model$monitorl1.parameters, JAGS.model$monitorl2.parameters),
                        burnin = burnin, sample = sample, thin = thin, adapt = adapt, jags = jags, summarise = FALSE,
                        method="rjags")
    samples <- result$mcmc[[1]]
    # find the correct samples, in case the order of monitors is shuffled by JAGS
    n_p_l2 <- length(JAGS.model$monitorl2.parameters)
    index_l2_param<- array(0,dim = c(n_p_l2,1))
    for (i in 1:n_p_l2)
      index_l2_param[i] <- which(colnames(result$mcmc[[1]]) == JAGS.model$monitorl2.parameters[i])
    if (length(index_l2_param) > 1)
      samples_l2_param <- result$mcmc[[1]][,index_l2_param]
    else
      samples_l2_param <- matrix(result$mcmc[[1]][,index_l2_param], ncol = 1)
    colnames(samples_l2_param) <- colnames(result$mcmc[[1]])[index_l2_param]

    n_p_l1 <- length(JAGS.model$monitorl1.parameters)
    index_l1_param<- array(0,dim = c(n_p_l1,1))
    for (i in 1:n_p_l1)
      index_l1_param[i] <- which(colnames(result$mcmc[[1]]) == JAGS.model$monitorl1.parameters[i])
    if (length(index_l1_param) > 1)
      samples_l1_param <- result$mcmc[[1]][,index_l1_param]
    else
      samples_l1_param <- matrix(result$mcmc[[1]][,index_l1_param], ncol = 1)
    colnames(samples_l1_param) <- colnames(result$mcmc[[1]])[index_l1_param]
    
    cat('Constructing ANOVA/ANCOVA tables...\n')
    anova.table <- table.ANCOVA(samples_l1_param, dMatrice$X, dMatrice$Z, samples_l2_param) # for ancova models
    coef.tables <- table.coefficients(samples_l2_param, JAGS.model$monitorl2.parameters, colnames(dMatrice$X), colnames(dMatrice$Z), 
                                      attr(dMatrice$X, 'assign') + 1, attr(dMatrice$Z, 'assign') + 1)
    pvalue.table <- table.pvalue(coef.tables$coeff_table, coef.tables$row_indices, l1_names = attr(dMatrice$X, 'varNames'), 
                                 l2_names = attr(dMatrice$Z, 'varNames'))
    conv <- conv.geweke.heidel(samples_l2_param, colnames(dMatrice$X), colnames(dMatrice$Z))
    class(conv) <- 'conv.diag'
    cat('Done...\n')
  }
  return(list(anova.table = anova.table,
              coef.tables = coef.tables,
              pvalue.table = pvalue.table, 
              conv = conv,
              dMatrice = dMatrice, samples_l1_param = samples_l1_param, samples_l2_param = samples_l2_param, data = data, 
              mf1 = mf1, mf2 = mf2, JAGSmodel = JAGS.model$sModel, single_level = single_level, model_name = "BANOVA.Normal"))
}