qqchisq: Chi-square quantile-quantile plot
In tweeDEseq: RNA-seq data analysis using the Poisson-Tweedie family of distributions

Description Usage Arguments Details Value References See Also Examples

Make a chi-square quantile-quantile plot.

1 2	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 `stat`.
`normal`	logical; set to `TRUE` if the χ^2 statistics in `stat` should be transform into normal z-scores in order to improve the display of lower quantiles. For this purpose, this function uses the `zscoreGamma` function from the limma package. Default is set to `FALSE`.
`rangeExpected`	logical; set to `TRUE` if the displayed range of the observed χ^2 statistics is restricted to the range of their expected values. Default is set to `FALSE`.
`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 `plot` function.

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

compareCountDist 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)

## Not run: 
qqchisq(chi2gof)

## End(Not run)