ebayes_est {fabCI} | R Documentation |
Empirical Bayes estimation of hyperparameters
Description
Compute emprirical Bayes estimates of the error variance and distribution of the regression coefficients.
Usage
ebayes_est(y, X, emu = FALSE, dof = min(50, round(0.5 * (dim(X)[1] -
dim(X)[2]))))
Arguments
y |
a numeric vector of data |
X |
a design matrix |
emu |
(logical) estimate mean of coefficient (TRUE) or assume it is zero (FALSE)? |
dof |
degrees of freedom to use for the t-quantiles (the remainder go to adaptive estimation of the prior) |
Details
This function computes the adaptive FAB confidence interval for each coefficient in a linear regression model.
Value
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
Author(s)
Peter Hoff
[Package fabCI version 0.2 Index]