ft_gof {discretefit} | R Documentation |

## Simulated Freeman-Tukey (Hellinger-distance) goodness-of-fit test

### Description

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.

### Usage

```
ft_gof(x, p, reps = 10000, tolerance = 64 * .Machine$double.eps)
```

### Arguments

`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 |

### Value

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 |

### Examples

```
x <- c(15, 36, 17)
p <- c(0.25, 0.5, 0.25)
ft_gof(x, p)
```

*discretefit*version 0.1.2 Index]