Conduct the two-sided Kolmogorov Smirnov (KS) test for data sampled from a
continuous distribution. By comparing the largest difference between the empirical cumulative
distribution of the sample data and the theoretical distribution we can provide a test for the
the null hypothesis that the sample data comes from that theoretical distribution.
For more information on KS Test:
Convenience function to conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability
distribution equality. Currently supports the normal distribution, taking as parameters
the mean and standard deviation.
dataset - A Dataset or a DataFrame containing the sample of data to test
sampleCol - Name of sample column in dataset, of any numerical type
distName - a String name for a theoretical distribution, currently only support "norm".
params - Double* specifying the parameters to be used for the theoretical distribution.
For "norm" distribution, the parameters includes mean and variance.
DataFrame containing the test result for the input sampled data.
This DataFrame will contain a single Row with the following fields:
- pValue: Double
- statistic: Double
public static Dataset<Row> test(Dataset<?> dataset,