# omega: Omega calcularion In tatest: Two-Group Ta-Test

## Description

Omega (ω) function is a function that is used to estimate omega using simulate null data from negative bionomial distribution. Omege is a null rho that is used as a threshold for real rho. Simulation is dependent on the original data.

## Usage

 `1` ```omega(XA, XB, na, nb, m, alpha = 0.05, distr = "norm") ```

## Arguments

 `XA` a numeric vector values of condition A `XB` a numeric vector values of condition b `na` a numeric value, sample size in A. `nb` a numeric value, sample size in B `m` simulation number specified. `alpha` a numeric value, statistical cutoff. Default is 0.05. `distr` data distribution specified for data. For current version, we consider three distributions: normal, negative binomial (NB) and uniform. Default distribution is "norm". If user believe that the data follow negative binomial distribution, then set distr="NB" or "negative binomial" or if the data follow uniform distribution, then set distr="unif"or "uniform".

## Details

This function is to use simulated null data to calculate omega value with rho = 1.

## Value

return a numeric value

## Author(s)

Yuan-De Tan tanyuande@gmail.com

## References

Yuan-De Tan Anita M. Chandler, Arindam Chaudhury, and Joel R. Neilson(2015) A Powerful Statistical Approach for Large-scale Differential Transcription Analysis. Plos One. 2015 DOI: 10.1371/journal.pone.0123658.

`momega`, `rhov`
 ```1 2 3 4 5 6 7 8``` ```X<-c(112,122,108,127) Y<-c(302, 314,322,328) omega(XA=X, XB=Y, na=4, nb=4, m=2000, alpha = 0.05) # 0.9055152 omega(XA=X, XB=Y, na=4, nb=4, m=2000, alpha = 0.05,distr="NB") # 0.8995424 omega(XA=X, XB=Y, na=4, nb=4, m=2000, alpha = 0.05,distr="uniform") # 0.97194 ```