Extract posterior model probability estimates (either normalized
estimates or sampling frequencies) from BayesMfp
objects.
1  posteriors(BayesMfpObject, ind = 1)

BayesMfpObject 
a valid 
ind 

The vector of probability estimates.
Daniel Saban\'es Bov\'e
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## 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)
## this must not work:
## SoDA::muststop(posteriors(test, ind=2))
## 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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