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Extract posterior model probability estimates (either normalized
estimates or sampling frequencies) from BayesMfp
objects.
posteriors(BayesMfpObject, ind = 1)
The vector of probability estimates.
a valid BayesMfp
object,
containing the models the probabilites of which one wants to
estimate
ind = 1
means normalized posteriors, ind = 2
means sampling frequencies
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 (x1, max = 2) + bfp (x2, max = 2) + uc (x3), nModels = 100,
method="exhaustive")
## this works:
posteriors(test)
## only if we do model sampling there are model frequencies:
test2 <- BayesMfp (y ~ bfp (x1, max = 2) + bfp (x2, max = 2) + uc (x3), nModels = 100,
method="sampling")
posteriors(test2, ind=2)
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