# is.nbinom: is.nbinom In DnE: Distribution and Equation

## Description

judge if the data obeys negative binomial distribution

## Usage

 `1` ```is.nbinom(x, m, a, p0 = NULL, r0 = NULL) ```

## Arguments

 `x` data `m` the number of intervals you want to divide the data in, default value is 10 `a` significance level `p0` the pobability of success in each experiment `r0` the number of successful events you want to wait for

## Details

Given a set of observations from a certain distribution, this function is used to test whether the observations are from a distribution of negative binomial distribution or not. Usually, to ensure the function works well, the sample size needs to be large enough, i.e. the result will be stable if the sample size is larger than 100. The function will work better if the number of intervals you choose to divide the data in is between 10 and 20. This number cannot excess the number of given oberservations.

## Value

if the data possibly obeys negative binomial distribution, return a value named qchisq which represents the possibility. The larger qchisq is, the larger the possibility will be; else return -1.

## Note

please pay attention to the definition of parameters in our functions.

## Author(s)

JunYao Chen, CuiYi He, BoXian Wei

## References

ROBERT V. HOGG/ALLEN T. CRAIG (Fifth Edition) Introduction Mathematical Statistics.

`is.dt` , `DnE-package`
 ```1 2 3 4 5 6``` ```require(stats) examplecheck<-rnbinom(100,10,0.1) is.nbinom(examplecheck,10,0.05) ##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ```