omega {tatest} | R Documentation |
Omega calcularion
Description
Omega (\omega
) 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
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.
See Also
Examples
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)
#[1] 0.9055152
omega(XA=X, XB=Y, na=4, nb=4, m=2000, alpha = 0.05,distr="NB")
#[1] 0.8995424
omega(XA=X, XB=Y, na=4, nb=4, m=2000, alpha = 0.05,distr="uniform")
#[1] 0.97194