.powerLaw {CAGEr} | R Documentation

## .powerLaw

### Description

Private funtion for normalizing CAGE tag count to a referent power-law distribution.

### Usage

    .powerLaw(tag.counts, fitInRange = c(10, 1000), alpha = 1.25, T = 10^6)

### Arguments

`tag.counts` | Numerical values whose reverse cumulative distribution will be fitted to power-law (e.g. tag count or signal for regions, peaks, etc.)
`fitInRange` | Range in which the fitting is done (values outside of this range will not be considered for fitting)
`alpha` | Slope of the referent power-law distribution (the actual slope has negative sign and will be -1*alpha)
`T` | total number of tags (signal) in the referent power-law distribution.

### Details

S4 Methods are provided for integer vectors, Rle objects, data.frame objects and DataFrame objects, so that the most complex objects can be deconstructed in simpler parts, normalized and reconstructed.

### Value

Normalized values (vector of the same length as input values); i.e. what would be the value of input values in the referent distribution. Ouptut objects are numeric, possibly `Rle`-encoded or wrapped in `data.frames` or `DataFrames` according to the input.

### References

Balwierz, P. J., Carninci, P., Daub, C. O., Kawai, J., Hayashizaki, Y., Van Belle, W., Beisel, C., et al. (2009). Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data. Genome Biology, 10(7), R79.

[Package CAGEr version 2.0.2 Index]