watson.two.test: Watson's Two-Sample Test of Homogeneity

In circular: Circular Statistics

Watson's Two-Sample Test of Homogeneity

Description

Performs Watson's test for homogeneity on two samples of circular data.

Usage

watson.two.test(x, y, alpha=0)
## S3 method for class 'watson.two.test'
print(x, digits=4, ...)

Arguments

x	a vector. The object is coerced to class circular.
y	a vector. The object is coerced to class circular.
alpha	significance level of the test. Valid levels are 0.001, 0.01, 0.05, 0.1. This argument may be omitted, in which case, a range for the p-value will be returned.
digits	integer indicating the precision to be used.
...	further arguments passed to or from other methods.

Details

Watson's two-sample test of homogeneity is performed, and the results are printed. If alpha is specified and non-zero, the test statistic is printed along with the critical value and decision. If alpha is omitted, the test statistic is printed and a range for the p-value of the test is given.

Critical values for the test statistic are obtained using the asymptotic distribution of the test statistic. It is recommended to use the obtained critical values and ranges for p-values only for combined sample sizes in excess of 17. Tables are available for smaller sample sizes and can be found in Mardia (1972) for instance.

Value

a list with statistic, alpha and the number of observations of the first and second sample.

Author(s)

Claudio Agostinelli and Ulric Lund

References

Jammalamadaka, S. Rao and SenGupta, A. (2001). Topics in Circular Statistics, Section 7.5, World Scientific Press, Singapore.

Examples

# Perform a two-sample test of homogeneity on two
# simulated data sets.
data1 <- rvonmises(n=20, mu=circular(0), kappa=3)
data2 <- rvonmises(n=20, mu=circular(pi), kappa=2)
watson.two.test(data1, data2, alpha=0.05)
watson.two.test(data1, data2)

circular documentation built on Sept. 8, 2023, 6:03 p.m.