# negbin.pow: Estimates power for given sample size using simulation from... In RSPS: RNA-Seq Power Simulation

## Description

The function provides estimate of power for given sample size when there is over-dispersion. The data is simulated from Negative Binomial distribution.

## Usage

 `1` ```negbin.pow(n, lambda1, k, disp, alpha, seed, numsim, monitor, sig) ```

## Arguments

 `n` A vector of positive integers representing the sample size `lambda1` Mean count under the null distribution. It can be a vector. `k` Fold change desired under the alternative distribution. It can be a vector. `disp` The over-dispersion parameter. 1 represent no pver-dispersion and values above one represent over-dispersion. `alpha` Type I error rate: a value between 0 and 1. It can be a vector. `seed` Value of seed to ensure reproducibility of results. `numsim` Number of simulations. 1000 is recommended. `monitor` If TRUE, it allows us to view the progress of the function. `sig` Number of significant digits after decimal.

## Details

The test statistic used is the scaled difference. Please contact the authors for more details on algorithm.

## Value

 ` Mean.Null ` Mean Count under Null distribution. ` Effect.Size ` Fold Change Under the alternate hypothesis. ` Disp.Par ` Over-dispersion parameter. ` Power ` Estimated Power. ` Std.Err ` Standard Error.

## Note

The alternative ecological parameterization is used for Negative binomial distribution.

Milan Bimali

## References

None

 ```1 2 3 4 5 6 7 8 9``` ```power.negbin <- negbin.pow(n=c(5,10),lambda1=c(3,5), k=c(2,3),disp=2,alpha=0.001,seed = 20, numsim=100,monitor=TRUE) power.plot(power.negbin) # Another example (takes longer to run) #power.negbin <- negbin.pow(n=c(3,5,10,15),lambda1=c(3,5), #k=c(2,2.5,3),disp=2,alpha=0.001,seed = 20, #numsim=1000,monitor=TRUE) #head(power.negbin) ```