Description Usage Arguments Details Value Author(s) References Examples

View source: R/watson.wheeler.test.R

Performs the Watson-Wheeler test for homogeneity on two or more samples of circular data.

1 2 3 4 5 6 7 8 9 10 | ```
watson.wheeler.test(x, ...)
## Default S3 method:
watson.wheeler.test(x, group, ...)
## S3 method for class 'list'
watson.wheeler.test(x, ...)
## S3 method for class 'formula'
watson.wheeler.test(formula, data, ...)
``` |

`x` |
a vector of angles (coerced to class |

`group` |
a vector or factor object giving the groups for the corresponding elements of |

`formula` |
a formula of the form |

`data` |
an optional data.frame containing the variables in the formula |

`...` |
further arguments passed to or from other methods. |

The Watson-Wheeler (or Mardia-Watson-Wheeler, or uniform score) test is a non-parametric test to compare two or several samples. The difference between the samples can be in either the mean or the variance.

The *p*-value is estimated by assuming that the test statistic follows a chi-squared distribution. For this approximation to be valid, all groups must have at least 10 elements.

In the default method, `x`

is a vector of angles and `group`

must be a vector or factor object of the same length as `x`

giving the group for the corresponding elements of `x`

.

If `x`

is a list, its elements are taken as the samples to be compared.

In the `formula`

method, the angles and grouping elements are identified as the left and right hand side of the formula respectively.

All angles should be of class `circular`

and will be coerced as such if they are not.

A list with class `"htest"`

containing the following components:

`statistic` |
W, the statistic of the test, which is approximately distributed as a chi-squared. |

`parameter` |
the degrees of freedom for the chi-squared approximation of the statistic. |

`p.value` |
the p-value for the test. |

`method` |
a character string containing the name of the test. |

`data.name` |
a character string giving the name(s) of the data. |

Jean-Olivier Irisson

Batschelet, E (1981). Circular Statistics in Biology. chap 6.3, p. 104

Zar, J H (1999). Biostatistical analysis. section 27.5, p. 640

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