如何在R中创建累积分布表？

I have plotted cumulative distribution of speeds using ecdf but I also want to get the output of cumulative probability as a table like this:

我使用ecdf绘制了速度的累积分布,但我也希望将累积概率的输出作为这样的表格得出:

Speed  Cumulative Probability
40  0.20
45  0.45
55  0.51
60  0.70
70  0.90
80  1.00

For my data, when I use ecdf it gives me following (Note that 'cc' is my original data frame):

对于我的数据,当我使用ecdf它给我跟随(注意'cc'是我的原始数据框):

> ccf <- subset(cc, cc$svel>=55 & cc$Headway>=4)  
> cdf<-  ecdf(ccf$svel)
> cdf
Empirical CDF 
Call: ecdf(ccf$svel)
 x[1:356] =     55,  55.01,  55.02,  ...,  76.76,   76.8

How can I get the table like the example above? Please note that I tried 'cumsum' but it only gives the cumulative frequency whereas I need cumulative probability.

我怎样才能像上面的例子那样获得表格?请注意,我尝试了'cumsum',但它只给出累积频率,而我需要累积概率。

EDIT

Here's my data:

这是我的数据:

dput(ccf$svel) c(67.9, 67.62, 67.37, 67.19, 67.04, 66.93, 66.83, 66.74, 66.65, 66.55, 66.46, 66.36, 66.25, 66.12, 65.97, 61.12, 61.2, 61.29, 61.39, 61.49, 61.58, 61.66, 61.73, 61.79, 57.98, 57.73, 57.5, 57.29, 57.1, 56.92, 56.75, 56.59, 56.45, 56.32, 56.19, 58, 58.18, 58.36, 58.52, 58.69, 56.28, 56.19, 56.08, 55.96, 55.83, 55.68, 55.52, 55.34, 55.15, 58.58, 58.89, 59.17, 59.4, 59.58, 55.01, 55.14, 55.23, 55.3, 55.36, 55.41, 55.47, 55.53, 55.59, 55.66, 55.74, 55.83, 55.92, 56.03, 56.16, 56.3, 56.44, 56.58, 56.71, 56.82, 56.91, 56.98, 57.03, 57.06, 57.07, 57.07, 57.06, 57.04, 57.02, 55.05, 55.22, 55.39, 55.56, 55.73, 55.92, 56.11, 56.31, 56.53, 56.77, 57.02, 57.28, 57.54, 57.79, 58, 58.18, 58.32, 58.43, 58.5, 58.56, 58.6, 58.64, 58.68, 58.73, 58.8, 58.86, 58.92, 58.97, 59.01, 59.03, 59.05, 59.05, 59.04, 59.02, 58.99, 58.97, 58.95, 55.1, 55.39, 55.68, 55.97, 56.24, 56.48, 56.68, 56.82, 56.9, 56.94, 56.96, 56.97, 56.99, 57.02, 57.07, 57.14, 57.22, 57.3, 57.37, 57.41, 57.45, 57.48, 57.51, 57.56, 57.62, 57.69, 57.77, 57.86, 57.95, 58.06, 58.17, 58.29, 58.42, 58.53, 58.64, 58.74, 58.83, 58.91, 58.98, 55.01, 55.08, 55.15, 55.22, 55.3, 55.37, 55.45, 55.53, 55.62, 55.73, 55.85, 55.99, 56.14, 56.31, 56.49, 56.67, 56.87, 57.05, 57.22, 57.37, 57.51, 57.65, 57.79, 57.95, 58.13, 58.3, 58.47, 58.63, 58.78, 58.91, 59.03, 59.14, 59.24, 59.34, 59.43, 59.53, 59.62, 59.72, 59.81, 59.9, 59.98, 60.07, 60.15, 60.22, 60.31, 60.39, 60.47, 60.56, 60.65, 60.75, 60.86, 60.98, 61.11, 61.24, 61.39, 61.54, 61.71, 61.89, 62.09, 62.31, 62.56, 62.84, 63.14, 63.46, 63.78, 64.08, 64.81, 64.84, 64.85, 64.87, 64.89, 64.92, 64.94, 64.97, 65, 65.02, 65.04, 65.07, 65.11, 65.15, 65.17, 65.18, 65.17, 65.15, 65.13, 65.1, 65.06, 65.01, 64.96, 64.9, 64.84, 64.79, 64.76, 55.04, 55.15, 55.25, 55, 55.23, 55.45, 55.68, 55.9, 56.69, 56.74, 55, 55, 55, 55, 55, 55.01, 55.26, 55.51, 55.77, 56.02, 56.28, 56.56, 56.84, 57.13, 57.42, 57.7, 57.98, 58.25, 58.49, 58.73, 58.94, 59.13, 59.29, 59.4, 59.48, 59.5, 59.48, 59.42, 59.31, 59.17, 59, 58.8, 58.6, 58.38, 58.17, 57.96, 57.77, 57.59, 57.44, 57.31, 57.21, 57.13, 57.07, 57.04, 57.03, 57.04, 57.07, 57.11, 57.18, 57.26, 57.34, 57.43, 57.51, 57.59, 57.68, 57.78, 57.88, 57.99, 58.08, 58.16, 58.22, 58.27, 58.3, 58.31, 58.31, 58.3, 58.27, 58.25, 58.22, 58.18, 58.14, 58.08, 58.01, 57.93, 57.84, 57.72, 57.59, 57.43, 57.27, 57.1, 56.93, 56.77, 56.63, 56.5, 56.38, 56.28, 56.19, 56.12, 56.05, 55.99, 55.94, 55.9, 55.88, 55.86, 55.85, 55.86, 55.87, 55.89, 55.9, 55.91, 55.91, 55.88, 55.84, 55.78, 55.71, 55.63, 55.56, 55.5, 55.45, 55.4, 55.37, 55.34, 55.32, 55.3, 55.29, 55.27, 55.26, 55.26, 55.25, 55.25, 55.26, 55.26, 55.27, 55.28, 55.29, 55.31, 55.33, 55.36, 55.39, 55.02, 55.07, 55.12, 55.16, 55.21, 55.26, 55.31, 55.04, 55.21, 55.38, 55.54, 55.71, 55.88, 56.05, 56.21, 56.38, 56.54, 56.71, 56.88, 57.04, 57.2, 57.35, 55.46, 55.59, 55.74, 55.92, 56.11, 56.32, 56.54, 56.77, 57.02, 57.28, 55.22, 55.28, 55.35, 55.42, 55.5, 55.58, 55.68, 55.78, 55.88, 56, 55.15, 55.45, 55.72, 55.94, 56.11, 56.22, 56.29, 56.33, 56.36, 56.4, 56.45, 56.51, 56.59, 56.69, 56.81, 56.95, 57.11, 57.27, 57.44, 57.61, 57.78, 57.95, 58.12, 58.29, 58.46, 58.63, 58.79, 58.94, 59.08, 59.21, 59.32, 59.41, 55.13, 55.3, 55.47, 55.65, 55.83, 56.02, 56.22, 56.43, 56.66, 56.9, 55.17, 56.02, 56.11, 56.21, 56.32, 56.42, 56.52, 57.18, 57.29, 57.42, 76.27, 76.28, 76.3, 76.33, 76.37, 76.41, 76.47, 76.54, 76.62, 76.7, 76.76, 76.8, 76.8, 55.08, 55.16, 55.24, 55.32, 55.4, 55.48, 55.12, 55.39, 55.67, 55.94, 56.21, 56.47, 56.72, 56.97, 57.19, 57.4, 57.58, 57.73, 57.87, 57.99, 58.11)

dput(ccf $ svel)c(67.9,67.62,67.37,67.19,67.04,66.93,66.83,66.74,66.65,66.55,66.46,66.36,66.25,66.12,65.97,61.12,61.2,61.29,61.39,61.49,61.58, 61.66,61.73,61.79,57.98,57.73,57.5,57.29,57.1,56.92,56.75,56.59,56.45,56.32,56.19,58,58.18,58.36,58.52,58.69,56.28,56.19,56.08,55.96,55.83,55.68, 55.52,55.34,55.15,58.58,58.89,59.17,59.4,59.58,55.01,55.14,55.23,55.3,55.36,55.41,55.47,55.53,55.59,55.66,55.74,55.83,55.92,56.03,56.16,56.3,56.44, 56.58,56.71,56.82,56.91,56.98,57.03,57.06,57.07,57.07,57.06,57.04,57.02,55.05,55.22,55.39,55.56,55.73,55.92,56.11,56.31,56.53,56.77,57.02,57.28,57.54, 57.79,58,58.18,58.32,58.43,58.5,58.56,58.6,58.64,58.68,58.73,58.8,58.86,58.92,58.97,59.01,59.03,59.05,59.05,59.04,59.02,58.99,58.97,58.95,55.1, 55.39,55.68,55.97,56.24,56.48,56.68,56.82,56.9,56.94,56.96,56.97,56.99,57.02,57.07,57.14,57.22,57.3,57.37,57.41,57.45,57.48,57.51,5 7.56,57.62,57.69,57.77,57.86,57.95,58.06,58.17,58.29,58.42,58.53,58.64,58.74,58.83,58.91,58.98,55.01,55.08,55.15,55.22,55.3,55.37,55.45,55.53,55.62, 55.73,55.85,55.99,56.14,56.31,56.49,56.67,56.87,57.05,57.22,57.37,57.51,57.65,57.79,57.95,58.13,58.3,58.47,58.63,58.78,58.91,59.03,59.14,59.24,59.34, 59.43,59.53,59.62,59.72,59.81,59.9,59.98,60.07,60.15,60.22,60.31,60.39,60.47,60.56,60.65,60.75,60.86,60.98,61.11,61.24,61.39,61.54,61.71,61.89,62.09, 62.31,62.56,62.84,63.14,63.46,63.78,64.08,64.81,64.84,64.85,64.87,64.89,64.92,64.94,64.97,65,65.02,65.04,65.07,65.11,65.15,65.17,65.18,65.17,65.15, 65.13,65.1,65.06,65.01,64.96,64.9,64.84,64.79,64.76,55.04,55.15,55.25,55,55.23,55.45,55.68,55.9,56.69,56.74,55,55,55,55,55,55.01, 55.26,55.51,55.77,56.02,56.28,56.56,56.84,57.13,57.42,57.7,57.98,58.25,58.49,58.73,58.94,59.13,59.29,59.4,59.48,59.5,59.48,59.42,59 .31,59.17,59,58.8,58.6,58.38,58.17,57.96,57.77,57.59,57.44,57.31,57.21,57.13,57.07,57.04,57.03,57.04,57.07,57.11,57.18,57.26,57.34,57.43,57.51 ,57.59,57.68,57.78,57.88,57.99,58.08,58.16,58.22,58.27,58.3,58.31,58.31,58.3,58.27,58.25,58.22,58.18,58.14,58.08,58.01,57.93,57.84,57.72,57.59,57.43 ,57.27,57.1,56.93,56.77,56.63,56.5,56.38,56.28,56.19,56.12,56.05,55.99,55.94,55.9,55.88,55.86,55.85,55.86,55.87,55.89,55.9,55.91,55.91,55.88,55.84 ,55.78,55.71,55.63,55.56,55.5,55.45,55.4,55.37,55.34,55.32,55.3,55.29,55.27,55.26,55.26,55.25,55.25,55.26,55.26,55.27,55.28,55.29,55.31,55.33,55.36 ,55.39,55.02,55.07,55.12,55.16,55.21,55.26,55.31,55.04,55.21,55.38,55.54,55.71,55.88,56.05,56.21,56.38,56.54,56.71,56.88,57.04,57.2,57.35,55.46,55.59 ,55.74,55.92,56.11,56.32,56.54,56.77,57.02,57.28,55.22,55.28,55.35,55.42,55.5,55.58,55.68,55.78,55.88,56,55.15,55.45,55.72, 55.94,56.11,56.22,56.29,56.33,56.36,56.4,56.45,56.51,56.59,56.69,56.81,56.95,57.11,57.27,57.44,57.61,57.78,57.95,58.12,58.29,58.46,58.63,58.79,58.94, 59.08,59.21,59.32,59.41,55.13,55.3,55.47,55.65,55.83,56.02,56.22,56.43,56.66,56.9,55.17,56.02,56.11,56.21,56.32,56.42,56.52,57.18,57.29,57.42,76.27, 76.28,76.3,76.33,76.37,76.41,76.47,76.54,76.62,76.7,76.76,76.8,76.8,55.08,55.16,55.24,55.32,55.4,55.48,55.12,55.39,55.67,55.94,56.21,56.47,56.72, 56.97,57.19,57.4,57.58,57.73,57.87,57.99,58.11)

1 个解决方案

#1

Here is a function that will do it:

这是一个可以执行此操作的函数:

cumprob <- function(y) {
  fun <- function(y, x) length(y[y<x])/length(y)
  prob<-sapply(y, fun, y=y)
  data<- data.frame(value=unique(y[order(y)]), prob=unique(prob[order(prob)]))
}

Testing in your data (here I called it data):

测试你的数据(这里我称之为数据):

cp<-cumprob(data)
head(cp)
  value       prob
1 55.00 0.00000000
2 55.01 0.01156069
3 55.02 0.01734104
4 55.04 0.01926782
5 55.05 0.02312139
6 55.07 0.02504817

Plot:

plot(cp)

Another quick and informal way I find pretty handy is using the hist function to automatically cut the data and get the midpoints.

我觉得非常方便的另一种快速和非正式的方法是使用hist函数自动剪切数据并获得中点。

Using your data as data:

将您的数据用作数据:

h <- hist(data)
cum.prob <- data.frame(value=h$mids, prob=cumsum(h$counts)/sum(h$counts))

That gives you:

这给你:

   cum.prob
   value      prob
1     55 0.2793834
2     57 0.6319846
3     59 0.8285164
4     61 0.8786127
5     63 0.8921002
6     65 0.9479769
7     67 0.9749518
8     69 0.9749518
9     71 0.9749518
10    73 0.9749518
11    75 0.9749518
12    77 1.0000000