missRanger returning OOB error

missRanger

function (data, maxiter = 10L, pmm.k = 0L, seed = NULL, verbose = 1, returnOOB = F, ...){
  if (verbose > 0) {
    cat("\nMissing value imputation by chained tree ensembles\n")
  }
  stopifnot(is.data.frame(data), dim(data) >= 1L, is.numeric(maxiter), 
            length(maxiter) == 1L, maxiter >= 1L, is.numeric(pmm.k), 
            length(pmm.k) == 1L, pmm.k >= 0L, !(c("formula", "data", 
                                                  "write.forest", "probability", "split.select.weights", 
                                                  "dependent.variable.name", "classification") %in% 
                                                  names(list(...))))
  if (!is.null(seed)) {
    set.seed(seed)
  }
  allVars <- names(which(vapply(data, function(z) (is.factor(z) || 
                                                     is.numeric(z)) && any(!is.na(z)), TRUE)))
  if (verbose > 0 && length(allVars) < ncol(data)) {
    cat("\n  Variables ignored in imputation (wrong data type or all values missing: ")
    cat(setdiff(names(data), allVars), sep = ", ")
  }
  stopifnot(length(allVars) > 1L)
  data.na <- is.na(data[, allVars, drop = FALSE])
  count.seq <- sort(colMeans(data.na))
  visit.seq <- names(count.seq)[count.seq > 0]
  if (!length(visit.seq)) {
    return(data)
  }
  verboseDigits <- 4
  j <- 1L
  predError <- rep(1, length(visit.seq))
  names(predError) <- visit.seq
  crit <- TRUE
  completed <- setdiff(allVars, visit.seq)
  if (verbose >= 2) {
    cat("\n", abbreviate(visit.seq, minlength = verboseDigits + 
                           2), sep = "\t")
  }
  while (crit && j <= maxiter) {
    if (verbose > 0) {
      cat("\niter ", j, ":\t", sep = "")
    }
    data.last <- data
    predErrorLast <- predError
    for (v in visit.seq) {
      v.na <- data.na[, v]
      if (length(completed) == 0L) {
        data[[v]] <- imputeUnivariate(data[[v]])
      }
      else {
        fit <- ranger(formula = reformulate(completed, 
                                            response = v), data = data[!v.na, union(v, 
                                                                                    completed)], ...)
        pred <- predict(fit, data[v.na, allVars])$predictions
        data[v.na, v] <- if (pmm.k) 
          pmm(xtrain = fit$predictions, xtest = pred, 
              ytrain = data[[v]][!v.na], k = pmm.k)
        else pred
        predError[[v]] <- fit$prediction.error/(if (fit$treetype == 
                                                    "Regression") 
          var(data[[v]][!v.na])
          else 1)
        if (is.nan(predError[[v]])) {
          predError[[v]] <- 0
        }
      }
      completed <- union(completed, v)
      if (verbose == 1) {
        cat(".")
      }
      else if (verbose >= 2) {
        cat(format(round(predError[[v]], verboseDigits), 
                   nsmall = verboseDigits), "\t")
      }
    }
    j <- j + 1L
    crit <- mean(predError) < mean(predErrorLast)
  }
  if (verbose > 0) {
    cat("\n")
  }
  if (j == 2L || (j == maxiter && crit)){
    if(returnOOB) {list(ximp = data, oob = predError)} else {data}
    
  } else {
    if(returnOOB) {list(ximp = data.last, oob = predError)} else {data.last}
  } 
}