Finds the prior parameter that maximizes the marginal likelihood given the prediction.
calc.a(y, mu, sf)calc.b(y, mu, sf)
calc.k(y, mu, sf)
A vector of observed gene counts.
A vector of predictions from expr.predict.
Vector of normalized size factors.
A vector with the optimized parameter and the negative log-likelihood.
calc.a returns a prior alpha parameter assuming constant
coefficient of variation. calc.b returns a prior beta parameter
assuming constant Fano factor. calc.k returns a prior variance
parameter assuming constant variance.