Cumulative distribution function in R
The probability that x will take a value less than or equal to x is the cumulative distribution function (CDF) of a random variable assessed at x.
The ecdf() function in R is used to calculate the cumulative distribution function. The ecdf() function in R is used to compute and plot the value of a numeric vector’s Empirical Cumulative Distribution Function.
The CDF data is returned by the ecdf() function, which takes the data vector as an input.
To calculate and plot a cumulative distribution function (CDF) in R, use the following basic syntax.
compute the data’s empirical CDF
Simply we can create a CDF plot
The examples below demonstrate how to utilize this syntax in practice.
Example 1: Calculate and plot the CDF of raw data
In R, you can calculate and plot a CDF of a random dataset using the following code:
Let’s create a some information
Now we can calculate the empirical CDF of the data
In R, plot the cumulative distribution function.
In order to plot a CDF function in R, we must first compute the CDF using the ecdf() function. The CDF plot is then plotted in the R language using the plot() function.
The result of the ecdf() method is passed to the plot function, which plots the CDF plot.
Let’s plot a CDF
plot(p, xlab='x', ylab='CDF', main='CDF Plot')
The x-axis shows the raw data values and the y-axis shows the corresponding CDF values.
Example 2: Calculate and plot a known distribution’s CDF
The following code demonstrates how to compute and plot a conventional normal distribution CDF.
curve(pnorm, from = -4, to = 4)
Alternatively, you can use ggplot2 to make the same plot:
library(ggplot2) ggplot(data.frame(x = c(-4, 4)), aes(x = x)) + stat_function(fun = pnorm)