Description Usage Arguments Details Value Author(s) References See Also Examples
Performs a Watson's goodness of fit test for the von Mises or circular uniform distribution.
1 2 3  watson.test(x, alpha=0, dist=c("uniform", "vonmises"))
## S3 method for class 'watson.test'
print(x, digits = 4, ...)

x 
a vector. The object is coerced to class

alpha 
significance level of the test. Valid levels are 0.01, 0.05, 0.1. This argument may be ommited, in which case, a range for the pvalue will be returned. 
dist 
distribution to test for. The default is the uniform
distribution. To test for the von Mises distribution, set 
digits 
integer indicating the precision to be used. 
... 
further arguments passed to or from other methods. 
If dist
= "uniform", Watson's onesample test for the circular uniform distribution is performed, and the results are printed. If alpha is specified and nonzero, 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 pvalue of the test is given.
If dist
= "vonmises", estimates of the population parameters are used to evaluate the von Mises distribution function at all data points, thereby arriving at a sample of approximately uniformly distributed data, if the original observations have a von Mises distribution. The onesample Watson test is then applied to the transformed data as above.
a list with the statistic, alpha, the number of observations, the
distribution and 'row' which is used by print.watson.test
to
evaluate the pvalue.
Claudio Agostinelli and Ulric Lund
Jammalamadaka, S. Rao and SenGupta, A. (2001). Topics in Circular Statistics, Section 7.2, World Scientific Press, Singapore.
Stephens, M. (1970). Use of the KolmogorovSmirnov, Cramervon Mises and related statistics without extensive tables. Journal of the Royal Statistical Society, B32, 115122.
range.circular
, kuiper.test
, rao.spacing.test
and rayleigh.test
1 2 3 4 5 6  # Generate data from the uniform distribution on the circle.
x < circular(runif(100, 0, 2*pi))
watson.test(x)
# Generate data from a von Mises distribution.
x < rvonmises(n=50, mu=circular(0), kappa=4)
watson.test(x, alpha=0.05, dist="vonmises")

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