getPosteriorParms | R Documentation |
Conditional on a fixed shrinkage factor t=g/(g+1), the posterior joint distribution of the effects and the regression variance is normal inverse gamma. With this function, you can compute the parameters of this distribution.
getPosteriorParms(x, shrinkage=x[[1]]$postExpectedShrinkage,
design = getDesignMatrix(x))
x |
a valid |
shrinkage |
shrinkage factor used in the computations (defaults
to the posterior expected shrinkage factor in the model |
design |
(centered) design matrix for the model |
A list with four parameters:
aStar |
the first parameter of the inverse gamma distribution |
VStar |
the covariance matrix part of the multivariate normal distribution |
mStar |
the expectation of the multivariate normal distribution |
bStar |
the second parameter of the inverse gamma distribution |
Daniel Saban\'es Bov\'e
## construct a BayesMfp object
set.seed(19)
x1 <- rnorm (n=15)
x2 <- rbinom (n=15, size=20, prob=0.5)
x3 <- rexp (n=15)
y <- rt (n=15, df=2)
test <- BayesMfp (y ~ bfp (x2, max = 4) + uc (x1 + x3), nModels = 200, method="exhaustive")
## now get the posterior parameters of the third best model
getPosteriorParms(test[3])
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