calc_loglik

the fitted g, or an ash object containing g

betahat

betahatsd

appropriate degrees of freedom for (t) distribution of betahat/sebetahat

indicates whether you want the likelihood under the EE or ET model

model

a scalar performing transformation on betahat and sebetahat, such that the model is $\beta_j / s_j^alpha ~ g()$,and eqnbetahat_j / s_j^alpha ~ N(0,(sebetahat^(1-alpha))^2) or student t distribution. When $alpha=0$ we have the EE model, when $alpha=1$, we have the ET model. $alpha$ should be in between 0 and 1, inclusively. Default is alpha=0.

alpha


Return the log-likelihood of the data betahat, with standard errors betahatsd, 
for a given g() prior on beta, or an ash object containing that


The main function in the ashr package is \code{\link{ash}}, which
should be examined for more details. For simplicity only the most commonly-
used options are documented under \code{\link{ash}}. For expert or interested
users the documentation for function \code{\link{ash.workhorse}} provides
documentation on all implemented options.

calc_loglik: Compute loglikelihood for data from ash fit

Description

Usage

Arguments

Details