kstest2
Two-sample Kolmogorov-Smirnov goodness-of-fit hypothesis test.
h = kstest2 (x1, x2) returns a test decision for the
null hypothesis that the data in vectors x1 and x2 are from the
same continuous distribution, using the two-sample Kolmogorov-Smirnov test.
The alternative hypothesis is that x1 and x2 are from different
continuous distributions. The result h is 1 if the test rejects the
null hypothesis at the 5% significance level, and 0 otherwise.
h = kstest2 (x1, x2, name, value)
returns a test decision for a two-sample Kolmogorov-Smirnov test with
additional options specified by one or more name-value pair arguments as
shown below.
| "alpha" | A value alpha between 0 and 1 specifying the significance level. Default is 0.05 for 5% significance. |
| "tail" | A string indicating the type of test: |
| "unequal" | "F(X1) not equal to F(X2)" (two-sided) [Default] | |
| "larger" | "F(X1) > F(X2)" (one-sided) | |
| "smaller" | "F(X1) < F(X2)" (one-sided) |
The two-sided test uses the maximum absolute difference between the cdfs of
the distributions of the two data vectors. The test statistic is
D* = max(|F1(x) - F2(x)|), where F1(x) is the proportion of x1
values less or equal to x and F2(x) is the proportion of x2 values less
than or equal to x. The one-sided test uses the actual value of the
difference between the cdfs of the distributions of the two data vectors
rather than the absolute value. The test statistic is
D* = max(F1(x) - F2(x)) or D* = max(F2(x) - F1(x)) for
tail = "larger" or "smaller", respectively.
[h, p] = kstest2 (…) also returns the
asymptotic p-value p.
[h, p, ks2stat] = kstest2 (…) also returns
the Kolmogorov-Smirnov test statistic ks2stat defined above for the
test type indicated by tail.
Source Code: kstest2