在R中绘制正常,左和右倾斜分布

时间:2013-11-27 22:13:40

标签: r plot statistics

我想创建3个图表用于说明目的:   - 正常分配   - 右倾斜分布   - 左偏分布

这应该是一项简单的任务,但我发现只有this link,它只显示正态分布。我该怎么办呢?

3 个答案:

答案 0 :(得分:25)

如果你不太正常,那么我建议你使用beta分布,它可以是对称的,右倾斜的,或者根据形状参数左倾斜。

hist(rbeta(10000,5,2))
hist(rbeta(10000,2,5))
hist(rbeta(10000,5,5))

答案 1 :(得分:13)

最后我得到了它,但是在你的帮助下,我依靠this site

 N <- 10000
 x <- rnbinom(N, 10, .5)
 hist(x, 
 xlim=c(min(x),max(x)), probability=T, nclass=max(x)-min(x)+1, 
   col='lightblue', xlab=' ', ylab=' ', axes=F,
   main='Positive Skewed')
lines(density(x,bw=1), col='red', lwd=3)

enter image description here

这也是一个有效的解决方案:

curve(dbeta(x,8,4),xlim=c(0,1))
title(main="posterior distrobution of p")

答案 2 :(得分:10)

只需使用fGarch包和这些功能:

dsnorm(x, mean = 0, sd = 1, xi = 1.5, log = FALSE)
psnorm(q, mean = 0, sd = 1, xi = 1.5)
qsnorm(p, mean = 0, sd = 1, xi = 1.5)
rsnorm(n, mean = 0, sd = 1, xi = 1.5)

** mean,sd,xi location parameter mean,scale parameter sd,skewness parameter xi。 实例

## snorm -
   # Ranbdom Numbers:
   par(mfrow = c(2, 2))
   set.seed(1953)
   r = rsnorm(n = 1000)
   plot(r, type = "l", main = "snorm", col = "steelblue")

   # Plot empirical density and compare with true density:
   hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue")
   box()
   x = seq(min(r), max(r), length = 201)
   lines(x, dsnorm(x), lwd = 2)

   # Plot df and compare with true df:
   plot(sort(r), (1:1000/1000), main = "Probability", col = "steelblue",
     ylab = "Probability")
   lines(x, psnorm(x), lwd = 2)

   # Compute quantiles:
   round(qsnorm(psnorm(q = seq(-1, 5, by = 1))), digits = 6)