Compute emprirical Bayes estimates of the error variance and distribution of the regression coefficients.
ebayes_est(y, X, emu = FALSE, dof = min(50, round(0.5 * (dim(X)[1] -
dim(X)[2]))))
a numeric vector of data
a design matrix
(logical) estimate mean of coefficient (TRUE) or assume it is zero (FALSE)?
degrees of freedom to use for the t-quantiles (the remainder go to adaptive estimation of the prior)
A list (s,sigma2,tau2,mu) where
s an estimate of the error standard deviation
sigma2 an estimate of the error variance, independent of s
tau2 an estimate of the coefficient variance, independent of s
mu an estimate of the coefficient mean, independent of s
This function computes the adaptive FAB confidence interval for each coefficient in a linear regression model.