| fabtCI {fabCI} | R Documentation |
FAB t-interval
Description
Computation of a 1-alpha FAB t-interval
Usage
fabtCI(y, psi = c(0, 100, 1, 2), alpha = 0.05)
Arguments
y |
a numeric vector with at least two non-missing values |
psi |
a length-four vector of hyperparameters for the prior |
alpha |
the type I error rate, so 1-alpha is the coverage rate |
Details
A FAB interval is the "frequentist" interval procedure
that is Bayes optimal: It minimizes the prior expected
interval width among all interval procedures with
exact 1-alpha frequentist coverage. This function computes
the FAB t-interval for the mean of a normal population with an
unknown variance, given a user-specified prior distribution
determined by psi. The prior is that the population mean
and variance are independently distributed as normal and
inverse-gamma random variables.
Referring to the elements of psi
as mu, t2, s20, nu0, the prior is determined as follows:
mu is the prior expectation of the mean
t2 is the prior variance of the mean
the population variance is inverse-gamma(nu0/2,nu0 s20/2)
Author(s)
Peter Hoff
Examples
y<-rnorm(10)
fabtCI(y,c(0,10,1,5))
fabtCI(y,c(0,1/10,1,5))
fabtCI(y,c(2,10,1,5))
fabtCI(y,c(0,1/10,1,5))