qqchisq {tweeDEseq} | R Documentation |
Make a chi-square quantile-quantile plot.
qqchisq(stat, df=1, normal=FALSE, rangeExpected=FALSE, obsQuantiles=c(0.50, 0.75, 0.95), ylim = NULL, ...)
stat |
vector of χ^2 statistics. |
df |
degrees of freedom of |
normal |
logical; set to |
rangeExpected |
logical; set to |
obsQuantiles |
observed quantiles to indicate by horizontal dash lines. By default, these are set to 50%, 75% and 95%. |
ylim |
they y limits of the plot. If 'NULL' (default), these will be obtained from the data. |
... |
further arguments to pass to the |
The main purpose of this function in the tweeDEseq
package is
to provide means to assess the goodness of fit of count data to the
negative binomial distribution. The main input argument stats
should be the output of gofTest
.
it returns invisibly a list with two components x
and y
corresponding to the coordinates of the plotted statistics.
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
## 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) ## Not run: qqchisq(chi2gof) ## End(Not run)