下面的函数有三种形式:

method = "mode"[默认值]:计算单模态向量的模式，否则返回NA Method = "nmodes":计算vector中模式的个数 Method = "modes":列出单模态或多模态向量的所有模态

modeav <- function (x, method = "mode", na.rm = FALSE)
{
  x <- unlist(x)
  if (na.rm)
    x <- x[!is.na(x)]
  u <- unique(x)
  n <- length(u)
  #get frequencies of each of the unique values in the vector
  frequencies <- rep(0, n)
  for (i in seq_len(n)) {
    if (is.na(u[i])) {
      frequencies[i] <- sum(is.na(x))
    }
    else {
      frequencies[i] <- sum(x == u[i], na.rm = TRUE)
    }
  }
  #mode if a unimodal vector, else NA
  if (method == "mode" | is.na(method) | method == "")
  {return(ifelse(length(frequencies[frequencies==max(frequencies)])>1,NA,u[which.max(frequencies)]))}
  #number of modes
  if(method == "nmode" | method == "nmodes")
  {return(length(frequencies[frequencies==max(frequencies)]))}
  #list of all modes
  if (method == "modes" | method == "modevalues")
  {return(u[which(frequencies==max(frequencies), arr.ind = FALSE, useNames = FALSE)])}  
  #error trap the method
  warning("Warning: method not recognised.  Valid methods are 'mode' [default], 'nmodes' and 'modes'")
  return()
}

这里有另一个解决方案:

freq <- tapply(mySamples,mySamples,length)
#or freq <- table(mySamples)
as.numeric(names(freq)[which.max(freq)])

我将使用density()函数来确定一个(可能是连续的)分布的平滑最大值:

function(x) density(x, 2)$x[density(x, 2)$y == max(density(x, 2)$y)]

其中x是数据集合。注意调节平滑的密度函数的调节参数。

在r邮件列表中发现了这个，希望对你有帮助。我也是这么想的。您将希望table()数据，排序，然后选择第一个名称。这有点粗俗，但应该有用。

names(sort(-table(x)))[1]

下面是一个查找模式的函数:

mode <- function(x) {
  unique_val <- unique(x)
  counts <- vector()
  for (i in 1:length(unique_val)) {
    counts[i] <- length(which(x==unique_val[i]))
  }
  position <- c(which(counts==max(counts)))
  if (mean(counts)==max(counts)) 
    mode_x <- 'Mode does not exist'
  else 
    mode_x <- unique_val[position]
  return(mode_x)
}

这是我的数据。返回完整表的逐行模式的表解决方案。我用它来推断行类。它负责data中新的set()函数。桌子，应该很快。虽然它不管理NA，但可以通过查看本页上的众多其他解决方案添加。

majorityVote <- function(mat_classes) {
  #mat_classes = dt.pour.centroids_num
  dt.modes <- data.table(mode = integer(nrow(mat_classes)))
  for (i in 1:nrow(mat_classes)) {
    cur.row <- mat_classes[i]
    cur.mode <- which.max(table(t(cur.row)))
    set(dt.modes, i=i, j="mode", value = cur.mode)
  }

  return(dt.modes)
}

可能的用法:

newClass <- majorityVote(my.dt)  # just a new vector with all the modes