Performing Kolmogorov-Smirnov tests

We use a one-sample Kolmogorov-Smirnov test to compare a sample with reference probability. A two-sample Kolmogorov-Smirnov test compares the cumulative distributions of two datasets. In this recipe, we will demonstrate how to perform a Kolmogorov-Smirnov test with R.

Getting ready

In this recipe, we will use the ks.test function from the stat package.

How to do it…

Perform the following steps to conduct a Kolmogorov-Smirnov test:

1. Validate whether the x dataset (generated with the rnorm function) is distributed normally with a one-sample Kolmogorov-Smirnov test:

   >set.seed(123)
   > x <-rnorm(50)
   >ks.test(x,"pnorm")
   
     One-sample
     Kolmogorov-Smirnov test
   
   data:  x
   D = 0.073034, p-value =
   0.9347
   alternative hypothesis: two-sided
   

2. Next, we can generate uniformly distributed sample data:

   >set.seed(123)
   > x <- runif(n=20, min=0, max=20)
   
   > y <- runif(n=20, min=0, max=20)
   

3. We first plot the ECDF of two generated data samples:

   >plot(ecdf...