Function to test the goodness of fit of every row in a matrix of counts
gofTest(counts, a = 0, mc.cores = 1)
matrix of counts
numeric scalar smaller than 1. The function will test whether the shape parameter is equal to the introduced 'a' (default is 0).
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
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.
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## 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))
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