ft_gof {discretefit} | R Documentation |
The ft_gof()
function implements Monte Carlo simulations to calculate p-values
based on the Freeman-Tukey statistic for goodness-of-fit tests for discrete
distributions. This statistic is also referred to as the Hellinger-distance.
Asymptotically, the Freeman-Tukey GOF test is identical to the Chi-squared
GOF test, but for smaller n, results may vary significantly.
ft_gof(x, p, reps = 10000, tolerance = 64 * .Machine$double.eps)
x |
a numeric vector that contains observed counts for each bin/category. |
p |
a vector of probabilities of the same length of x. An error is given if any entry of p is negative or if the sum of p does not equal one. |
reps |
an integer specifying the number of Monte Carlo simulations. The default is set to 10,000 which may be appropriate for exploratory analysis. A higher number of simulation should be selected for more precise results. |
tolerance |
sets an upper bound for rounding errors when evaluating
whether a statistic for a simulation is greater than or equal to the
statistic for the observed data. The default is identical to the tolerance
set for simulations in the |
A list with class "htest" containing the following components:
statistic |
the value of the Freeman-Tukey test statistic (W2) |
p.value |
the simulated p-value for the test |
method |
a character string describing the test |
data.name |
a character string give the name of the data |
x <- c(15, 36, 17)
p <- c(0.25, 0.5, 0.25)
ft_gof(x, p)