# compareCountDistributions: Compare count data distributions In tweeDEseq: RNA-seq data analysis using the Poisson-Tweedie family of distributions

## Description

Compares the empirical and estimated distributions for different count data models

## Usage

 `1` ``` compareCountDist(x, plot=TRUE, ...) ```

## Arguments

 `x` numeric vector containing the read counts. `plot` If `TRUE` (the default) then the plot with the ECDF function for the counts and the three different Poisson-Tweedie distributions is produced, otherwise no graphical output is given and this only makes sense if one is interested in the returned value (see value section below). `...` Further arguments to be passed to the plot function.

## Details

This function serves the purpose of comparing a empirical distribution of counts with three Poisson-Tweedie distributions arising from estimating mean, dispersion and setting a=1 for comparing against a Poisson, a=0 for comparing against a negative binomial and estimating the shape parameter a from data too. The legend shows the values of the a parameter and the P-value of the likelihood ratio test on whether the expression profile follows a negative binomial distribution (H_0:a=0).

## Value

List with the following components:

 `a` shape parameter estimated from the input data `x`. `p.value` P-value for the test that the data follows a negative binomial distribution, i.e., H_0:a=0.

## References

Esnaola M, Puig P, Gonzalez D, Castelo R and Gonzalez JR (2013). A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments. BMC Bioinformatics 14: 254

`qqchisq` `testShapePT`
 ```1 2 3 4 5 6``` ```# Generate 500 random counts following a Poisson Inverse Gaussian # distribution with mean = 20 and dispersion = 5 randomCounts <- rPT(n = 500, mu = 20, D = 5, a = 0.5) xx <- compareCountDist(randomCounts, plot=FALSE) xx ```