Optimizes variance

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)

Arguments

y	A vector of observed gene counts.
mu	A vector of predictions from expr.predict.
sf	Vector of normalized size factors.

Value

A vector with the optimized parameter and the negative log-likelihood.

Details

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.