R/vim.ensemble.R

In logicFS: Identification of SNP Interactions

vim.ensemble <- function (log.out, score = c("DPO", "Conc", "Brier"), adjusted = FALSE, 
                          neighbor = NULL, addMatImp = TRUE, addInfo = FALSE)
{
  if (!is(log.out, "logicBagg")) 
    stop("log.out must be an object of class logicBagg.")
  type <- log.out$type
  score <- match.arg(score)
  if(type != 4)
    stop("Ensemble measure only available in the survival case.")
  list.primes <- logic.pimp(log.out)
  allNull <- function (x) all(sapply(x, is.null))
  if (any(sapply(list.primes, allNull))){
    whichNull <- which(sapply(list.primes, allNull))
    log.out$logreg.model <- log.out$logreg.model[-whichNull]
    log.out$inbagg <- log.out$inbagg[-whichNull]
    list.primes <- logic.pimp(log.out)
    warning("Since ", length(whichNull), " of the models contain no variables, ", 
            "they are removed.", call. = FALSE)
  }
  B <- length(list.primes) 
  vec.primes <- unlist(lapply(list.primes, function (x) unique(unlist(x))))
  prop <- table(vec.primes)/B 
  vec.primes <- unique(vec.primes) 
  data <- as.data.frame(log.out$data)
  colnames(data) <- paste("X", 1:ncol(data), sep = "")
  mat.eval <- getMatEval(data, vec.primes, check = FALSE) 
  if (ncol(mat.eval) < length(vec.primes)){
    ids <- which(!vec.primes %in% colnames(mat.eval))
    mono <- vec.primes[ids]
    vec.primes <- vec.primes[-ids]
    rmMonoPI <- function (lpi, mono, type){
      if (type == 9) 
        lapply(lpi, function (x) x[!x %in% mono])
      else lpi[!lpi %in% mono]
    }
    for (i in 1:B) list.primes[[i]] <- lapply(list.primes[[i]], 
                                              rmMonoPI, mono = mono, type = type)
  } 
  inbagg <- log.out$inbagg
  if (any(table(unlist(inbagg)) ==  B))
    stop("There are observations that are inbagg in all ", B,
         " iterations of survivalFS.\n", "Either choose a larger",
         " number of iterations or select a non-ensemble importance measure.")
  if (!is.null(neighbor)){
    if (length(unique(unlist(neighbor))) != length(unlist(neighbor)))
      stop("There are SNPs that have more than one neighborhood.")
    set <- checkSet(NULL, ncol(log.out$data), colnames(log.out$data))
    set <- lapply(set, function (x) paste0("X", x))
  } else {
    set <- NULL
  }
  if (adjusted){
    vim <- getEnsembleAdjusted(log.out, vec.primes, list.primes, mat.eval, 
                               score, neighbor, set)
  } else if (!is.null(neighbor)){
    vim <- getEnsembleNeighbor(log.out, vec.primes, list.primes, mat.eval, 
                               score, neighbor, set)
  } else{
    vim <- getEnsemble(log.out, vec.primes, list.primes, 
                       mat.eval, score) 
  }
  if (addMatImp) 
    sfm <- vim[length(vim)]
  else
    sfm <- NULL
  vim <- vim[-length(vim)]
  names(vim) <- vec.primes
  if (score == "Brier") 
    vim <- (-1) * vim
  measure <- switch(which(c("DPO", "Conc", "Brier") %in% score), "EDPO", 
                    "EConc", "EBrier")
  param <- if (addInfo) 
    list(B = B, ntrees = log.out$ntrees, nleaves = log.out$nleaves, 
         sampling = log.out$sampling)
  else NULL
  if (any(c(!is.null(neighbor), adjusted))){
    idm <- as.numeric(!is.null(neighbor)) + 2 * as.numeric(adjusted)
    measure <- paste(measure, switch(idm, "\n (LD adjusted)", "\n (noise adjusted)", 
                                     "\n (LD and noise adjusted)"), sep = "")
    prop <- getProp(list.primes, vec.primes, mat.eval, set, neighbor, adjusted)
  } else{
    prop <- prop[vec.primes]
  }
  primes <- getNames(vec.primes, colnames(log.out$data))
  vim.out <- list(vim = vim, prop = prop, primes = primes, 
                  type = type, param = param, mat.imp = sfm, 
                  measure = measure, neighbor = neighbor,  
                  useN = TRUE, threshold = NULL, mu = NULL)
  class(vim.out) <- "logicFS"
  return(vim.out)
}