在R中,mean()和median()是标准函数,它们执行您所期望的功能。Mode()告诉您对象的内部存储模式,而不是参数中出现次数最多的值。但是是否存在一个标准库函数来实现向量(或列表)的统计模式?
当前回答
模式并不是在所有情况下都有用。所以函数应该处理这种情况。试试下面的函数。
Mode <- function(v) {
# checking unique numbers in the input
uniqv <- unique(v)
# frquency of most occured value in the input data
m1 <- max(tabulate(match(v, uniqv)))
n <- length(tabulate(match(v, uniqv)))
# if all elements are same
same_val_check <- all(diff(v) == 0)
if(same_val_check == F){
# frquency of second most occured value in the input data
m2 <- sort(tabulate(match(v, uniqv)),partial=n-1)[n-1]
if (m1 != m2) {
# Returning the most repeated value
mode <- uniqv[which.max(tabulate(match(v, uniqv)))]
} else{
mode <- "Two or more values have same frequency. So mode can't be calculated."
}
} else {
# if all elements are same
mode <- unique(v)
}
return(mode)
}
输出,
x1 <- c(1,2,3,3,3,4,5)
Mode(x1)
# [1] 3
x2 <- c(1,2,3,4,5)
Mode(x2)
# [1] "Two or more varibles have same frequency. So mode can't be calculated."
x3 <- c(1,1,2,3,3,4,5)
Mode(x3)
# [1] "Two or more values have same frequency. So mode can't be calculated."
其他回答
为了生成模式,我写了下面的代码。
MODE <- function(dataframe){
DF <- as.data.frame(dataframe)
MODE2 <- function(x){
if (is.numeric(x) == FALSE){
df <- as.data.frame(table(x))
df <- df[order(df$Freq), ]
m <- max(df$Freq)
MODE1 <- as.vector(as.character(subset(df, Freq == m)[, 1]))
if (sum(df$Freq)/length(df$Freq)==1){
warning("No Mode: Frequency of all values is 1", call. = FALSE)
}else{
return(MODE1)
}
}else{
df <- as.data.frame(table(x))
df <- df[order(df$Freq), ]
m <- max(df$Freq)
MODE1 <- as.vector(as.numeric(as.character(subset(df, Freq == m)[, 1])))
if (sum(df$Freq)/length(df$Freq)==1){
warning("No Mode: Frequency of all values is 1", call. = FALSE)
}else{
return(MODE1)
}
}
}
return(as.vector(lapply(DF, MODE2)))
}
让我们试试吧:
MODE(mtcars)
MODE(CO2)
MODE(ToothGrowth)
MODE(InsectSprays)
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]
假设你的观测值是来自实数的类,当你的观测值是2,2,3,3时,你期望模态为2.5,然后你可以用mode = l1 + I * (f1-f0) / (2f1 -f0 - f2)来估计模态,其中l1..最频繁类的下限,f1..最频繁类的频率,f0..在最频繁类之前的类的频率,f2..在最频繁类之后的类的频率,i..分类间隔,如在1,2,3中给出:
#Small Example
x <- c(2,2,3,3) #Observations
i <- 1 #Class interval
z <- hist(x, breaks = seq(min(x)-1.5*i, max(x)+1.5*i, i), plot=F) #Calculate frequency of classes
mf <- which.max(z$counts) #index of most frequent class
zc <- z$counts
z$breaks[mf] + i * (zc[mf] - zc[mf-1]) / (2*zc[mf] - zc[mf-1] - zc[mf+1]) #gives you the mode of 2.5
#Larger Example
set.seed(0)
i <- 5 #Class interval
x <- round(rnorm(100,mean=100,sd=10)/i)*i #Observations
z <- hist(x, breaks = seq(min(x)-1.5*i, max(x)+1.5*i, i), plot=F)
mf <- which.max(z$counts)
zc <- z$counts
z$breaks[mf] + i * (zc[mf] - zc[mf-1]) / (2*zc[mf] - zc[mf-1] - zc[mf+1]) #gives you the mode of 99.5
如果你想要最频繁的级别,并且你有多个最频繁的级别,你可以得到所有的级别,例如:
x <- c(2,2,3,5,5)
names(which(max(table(x))==table(x)))
#"2" "5"
下面是可以用来找到R中矢量变量的模式的代码。
a <- table([vector])
names(a[a==max(a)])
我还不能投票,但Rasmus Bååth的答案是我一直在寻找的。 但是,我将稍微修改一下,允许将分布限制在0到1之间。
estimate_mode <- function(x,from=min(x), to=max(x)) {
d <- density(x, from=from, to=to)
d$x[which.max(d$y)]
}
我们知道你可能根本不想约束你的分布,那么设置from=-"BIG NUMBER", to="BIG NUMBER"