What is a KS test in R?

What is a KS test in R?

A K-S Test quantifies a distance between the cumulative distribution function of the given reference distribution and the empirical distributions of given two samples, or between the empirical distribution of given two samples. The Kolmogorov-Smirnov test can be done very easily in R Programming.

What is a good KS score?

K-S should be a high value (Max =1.0) when the fit is good and a low value (Min = 0.0) when the fit is not good. When the K-S value goes below 0.05, you will be informed that the Lack of fit is significant.

What does a KS test show?

The two sample Kolmogorov-Smirnov test is a nonparametric test that compares the cumulative distributions of two data sets(1,2). The KS test report the maximum difference between the two cumulative distributions, and calculates a P value from that and the sample sizes.

How do you use Kolmogorov-Smirnov test?

The general steps to run the test are:

1. Create an EDF for your sample data (see Empirical Distribution Function for steps),
2. Specify a parent distribution (i.e. one that you want to compare your EDF to),
3. Graph the two distributions together.
4. Measure the greatest vertical distance between the two graphs.

How do you perform a KS test?

What is the Kolmogorov-Smirnov test used for?

The Kolmogorov-Smirnov test (Chakravart, Laha, and Roy, 1967) is used to decide if a sample comes from a population with a specific distribution. where n(i) is the number of points less than Yi and the Yi are ordered from smallest to largest value.

What is the D value in KS test?

The D statistic is simply the maximum of D– and D+. You can compare the statistic D to critical values of the D distribution, which appear in tables. If the statistic is greater than the critical value, you reject the null hypothesis that the sample came from the reference distribution.

How do you use KS test?

Are there estimated parameters for the KS test?

If a single-sample test is used, the parameters specified in … must be pre-specified and not estimated from the data. There is some more refined distribution theory for the KS test with estimated parameters (see Durbin, 1973), but that is not implemented in ks.test. Z. W. Birnbaum and Fred H. Tingey (1951).

Is there a distribution theory for the KS test?

There is some more refined distribution theory for the KS test with estimated parameters (see Durbin, 1973), but that is not implemented in ks.test . Z. W. Birnbaum and Fred H. Tingey (1951), One-sided confidence contours for probability distribution functions. The Annals of Mathematical Statistics, 22/4, 592–596.

What can you do with CRAN package ks?

CRAN – Package ks Kernel smoothers for univariate and multivariate data, including densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests.

How is a K-S test used in science?

A K-S Test quantifies a distance between the cumulative distribution function of the given reference distribution and the empirical distributions of given two samples, or between the empirical distribution of given two samples.