绘制R中数据集的概率密度/质量函数

Question

I have data set and i want to analysis this data by probability density function or probability mass function in R ,i used density function but it didn't gave me a probability.

my data like this:

"step","Time","energy"
1, 22469 , 392.96E-03
2, 22547 , 394.82E-03
3, 22828,400.72E-03
4, 21765, 383.51E-03
5, 21516, 379.85E-03
6, 21453, 379.89E-03
7, 22156, 387.47E-03
8, 21844, 384.09E-03
9 , 21250, 376.14E-03
10,  21703, 380.83E-03

I want to get PDF/PMF to energy vector ,the data we take into account are discrete by nature so i don't have special type for distribution the data.

Answer 1

Your data looks far from discrete to me. Expecting a probability when working with continuous data is plain wrong. density() gives you an empirical density function, which approximates the true density function. To prove it is a correct density, we calculate the area under the curve :

energy <- rnorm(100)
dens <- density(energy)
sum(dens$y)*diff(dens$x[1:2])
[1] 1.000952

Given some rounding error. the area under the curve sums up to one, and hence the outcome of density() fulfills the requirements of a PDF.

Use the probability=TRUE option of hist or the function density() (or both)

eg :

hist(energy,probability=TRUE)
lines(density(energy),col="red")

gives

If you really need a probability for a discrete variable, you use:

 x <- sample(letters[1:4],1000,replace=TRUE)
 prop.table(table(x))
x
    a     b     c     d 
0.244 0.262 0.275 0.219 

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Edit : illustration why the naive count(x)/sum(count(x)) is not a solution. Indeed, it's not because the values of the bins sum to one, that the area under the curve does. For that, you have to multiply with the width of the 'bins'. Take the normal distribution, for which we can calculate the PDF using dnorm() . Following code constructs a normal distribution, calculates the density, and compares with the naive solution :

x <- sort(rnorm(100,0,0.5))
h <- hist(x,plot=FALSE)
dens1 <-  h$counts/sum(h$counts)
dens2 <- dnorm(x,0,0.5)

hist(x,probability=TRUE,breaks="fd",ylim=c(0,1))
lines(h$mids,dens1,col="red")
lines(x,dens2,col="darkgreen")

Gives :

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The cumulative distribution function

In case @Iterator was right, it's rather easy to construct the cumulative distribution function from the density. The CDF is the integral of the PDF. In the case of the discrete values, that simply the sum of the probabilities. For the continuous values, we can use the fact that the intervals for the estimation of the empirical density are equal, and calculate :

cdf <- cumsum(dens$y * diff(dens$x[1:2]))
cdf <- cdf / max(cdf) # to correct for the rounding errors
plot(dens$x,cdf,type="l")

Gives :