Directional (version 2.4)

Goodness of fit test for grouped data: 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, $\kappa$.

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.

Value

A list including: A list including:

Details

When you have grouped data, you can test whether the data come from the von Mises-Fisher distribution or from a uniform distribution.

References

Arthur Pewsey, Markus Neuhauser, and Graeme D. Ruxton (2013). Circular Statistics in R.

See Also

pvm, circ.summary, rvonmises

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)