R/summary.map.r

In synbreed: Framework for the Analysis of Genomic Prediction Data using R

# summary for maker maps

summaryGenMap <- function(map, cores=1){
  multiLapply <- function(x,y,...,cores=cores){
    if(.Platform$OS.type == "windows" & cores>1){
      cl <- makeCluster(min(cores, detectCores()))
      registerDoParallel(cl)
      parLapply(cl,x,y,...)
      stopCluster(cl)
    } else {
      mclapply(x,y,...,mc.cores=cores)
    }
  }
  # information from arguments
  if(class(map)=="gpData") map <- map$map
  if(is.null(map)) stop("No map available")
  chr <- map$chr
  pos <- map$pos
  # extract information
  # number of markers
  len <- tapply(pos,chr,length)
  rge <- tapply(pos,chr,max,na.rm=TRUE)-tapply(pos,chr,min,na.rm=TRUE)
  # differences of markers on each chr
  diffs <- tapply(pos[!is.na(pos)],chr[!is.na(pos)],diff,na.rm=TRUE)
  avDist <- as.numeric(multiLapply(diffs,mean,na.rm=TRUE, cores=cores))
  maxDist <- as.numeric(multiLapply(diffs,max,na.rm=TRUE, cores=cores))
  minDist <- as.numeric(multiLapply(diffs,min,na.rm=TRUE, cores=cores))
  # return data.frame
  ret <- data.frame(noM = len, length=rge, avDist = avDist, maxDist = maxDist, minDist = minDist,row.names=names(len))
  # keep same order as original map
  ret <- ret[order(order(unique(chr))),]
  # sum over all chr
  all <- data.frame(noM = sum(ret$noM,na.rm=TRUE),length= sum(ret$length,na.rm=TRUE),avDist=weighted.mean(ret$avDist,ret$noM,na.rm=TRUE),maxDist=max(ret$maxDist,na.rm=TRUE),minDist=min(ret$minDist,na.rm=TRUE))
  rownames(all) <- paste(rownames(ret)[1],"-",rownames(ret)[nrow(ret)])
  ret <- rbind(ret,all)
  return(ret)
}