Directional (version 4.0)

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:

info

A vector with two elements, the test statistic value and the bootstrap p-value.

runtime

The runtime of the procedure.

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

# NOT RUN {
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)
# }