# group.gof: Goodness of fit test for grouped data In Directional: Directional Statistics

## Description

Goodness of fit test for grouped data.

## Usage

 `1` ```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, κ. `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

A list including:

 `info` A vector with two elements, the test statistic value and the bootstrap p-value. `runtime` The runtime of the procedure.

## Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Giorgos Athineou <[email protected]>

## References

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

```pvm, circ.summary, rvonmises ```
 ```1 2 3 4 5``` ```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) ```