Machine Learning with R Cookbook - Second Edition

How to do it...

Perform the following steps:

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

        > x = rnorm(50)        > ks.test(x,"pnorm")        Output:         One-sample Kolmogorov-Smirnov test        data: x        D = 0.1698, p-value = 0.0994        alternative hypothesis: two-sided

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

    > set.seed(3)     > 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(x), do.points = FALSE, verticals=T, xlim=c(0, 20))         > lines(ecdf(y), lty=3, do.points = FALSE, verticals=T)

The ecdf plot of two generated data samples

4. Finally, we apply a two-sample Kolmogorov-Smirnov ...