| Goodness of fit test for grouped data {Directional} | R Documentation |
Goodness of fit test for grouped data
Description
Goodness of fit test for grouped data.
Usage
group.gof(g, ni, m, k, dist = "vm", rads = FALSE, R = 999, ncores = 1)
Arguments
g |
A vector with the group points, either in radians or in degrees. |
ni |
The frequency of each or group class. |
m |
The mean direction in radians or in degrees. |
k |
The concentration parameter, |
dist |
The distribution to be tested, it can be either "vm" or "uniform". |
rads |
If the data are in radians, this should be TRUE and FALSE otherwise. |
R |
The number of bootstrap simulations to perform, set to 999 by default. |
ncores |
The number of cores to use. |
Details
When you have grouped data, you can test whether the data come from the von Mises-Fisher distribution or from a uniform distribution.
Value
This is an "htest"class object. Thus it returns a list including:
statistic |
The test statistic value. |
parameter |
Since this is a bootstrap based test, there are no degrees of freedom, hence this is "NA". |
p.value |
The p-value of the test. |
alternative |
A character with the alternative hypothesis. |
method |
A character with the test used. |
data.name |
A character vector with two elements. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Arthur Pewsey, Markus Neuhauser, and Graeme D. Ruxton (2013). Circular Statistics in R.
See Also
Examples
x <- rvonmises(100, 2, 10)
g <- seq(min(x) - 0.1, max(x) + 0.1, length = 6)
ni <- as.vector( table( cut(x, g) ) )
group.gof(g, ni, 2, 10, dist = "vm", rads = TRUE, R = 299, ncores = 1)
group.gof(g, ni, 2, 5, dist = "vm", rads = TRUE, R = 299, ncores = 1)