The documentation for blinreg() says that it:
Gives a simulated sample from the joint posterior distribution of the regression vector and the error standard deviation for a linear regression model with a noninformative or g prior.
Iām not sure if a g prior is what you mean by āinformative priorā? If so, then the information you want is in this part of the docs for blinreg():
Usage
blinreg(y,X,m,prior=NULL)
Arguments
|
|
y |
vector of responses |
X |
design matrix |
m |
number of simulations desired |
prior |
list with components c0 and beta0 of Zellner's g prior |
Reading this sort of documentation takes some getting used to, and this is a particularly terse example. That may be because this is a book companion package, and itās assumed you will be reading the book where more explanation may be offered (I donāt know, I donāt have a copy of that book at hand).
But putting together the two sections above, we can see that the default for the prior parameter is NULL. This is why in basic examples, you donāt even see the prior parameter used ā if a parameter has a default, you donāt have to specify it when calling the function unless you want something different from the default.
By inference (hereās where the docs could be clearer!), prior = NULL corresponds to an uninformative prior. To call blinreg() with a g prior, we need to follow the instructions above and provide a list with certain named components as the prior parameter, like:
prior = list(c0 =Ā , beta0 =Ā )
with your desired values or calculations in the blanks.
Iām afraid I donāt quite know what youāre looking for here. Can you describe what youāre confused about in more detail, or give a small example?