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 |

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.

1 2 3 4 5 6 7 8 9 | ```
## 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))
``` |

tweeDEseq documentation built on Jan. 5, 2019, 7:06 p.m.

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