Goodness of fit test for grouped data
Goodness of fit test for grouped data.
group.gof(g, ni, m, k, dist = "vm", rads = FALSE, R = 999, ncores = 1)
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. |
When you have grouped data, you can test whether the data come from the von Mises-Fisher distribution or from a uniform distribution.
A list including:
info |
A vector with two elements, the test statistic value and the bootstrap p-value. |
runtime |
The runtime of the procedure. |
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>
Arthur Pewsey, Markus Neuhauser, and Graeme D. Ruxton (2013). Circular Statistics in R.
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)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.