gofTest: Test the goodness of fit of every row in a matrix of counts
In tweeDEseq: RNA-seq data analysis using the Poisson-Tweedie family of distributions

Description Usage Arguments Details Value References See Also Examples

Function to test the goodness of fit of every row in a matrix of counts

1	gofTest(counts, a = 0, mc.cores = 1)

`counts`	matrix of counts
`a`	numeric scalar smaller than 1. The function will test whether the shape parameter is equal to the introduced 'a' (default is 0).
`mc.cores`	number of cpu cores to be used. This option is only available when the 'multicore' package is installed and loaded first. In such a case, if the default value of `mc.cores=1` is not changed, all available cores will be used.

By default a = 0, and therefore the function tests for every row of the input matrix of counts whether the count data follows a Negative-Binomial distribution. In this case, a Likelihood Ratio Test is performed. When the given value for 'a' is different from 0, a Wald test is performed. This function calls testShapePT.

a vector of statistics that follows a χ^2 distribution with one degree of freedom under the null hypothesis.

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

A.H. El-Shaarawi, R. Zhu, H. Joe (2010). Modelling species abundance using the Poisson-Tweedie family. Environmetrics 22, pages 152-164.

P. Hougaard, M.L. Ting Lee, and G.A. Whitmore (1997). Analysis of overdispersed count data by mixtures of poisson variables and poisson processes. Biometrics 53, pages 1225-1238.

testShapePT

## Generate a random matrix of counts
counts <- matrix(rPT(n=2000, a=0.5, mu=10, D=5), nrow=20)

## Perform the goodness-of-fit tests for every row in the matrix
chi2gof <- gofTest(counts)

## Calculate and sort the corresponding P-values for the
## null hypothesis that counts follow a negative binomial distribution
sort(pchisq(chi2gof, df=1, lower.tail=FALSE))

 [1] 6.150545e-05 3.547492e-04 2.508097e-02 3.031987e-02 6.132715e-02
 [6] 9.799692e-02 1.051269e-01 1.161962e-01 1.176889e-01 1.584903e-01
[11] 1.773903e-01 2.861552e-01 2.882200e-01 3.280658e-01 4.236746e-01
[16] 4.420108e-01 5.204521e-01 5.945177e-01 6.779848e-01 8.364026e-01