#sample, with keeping the best 200 models:
data(datafls)
mm=bms(datafls,burn=1000,iter=5000,nmodel=200)
#standard BMA PIPs and coefficients from the MCMC sampling chain, based on
# ...how frequently the models were drawn
coef(mm)
#standardized coefficients, ordered by index
coef(mm,std.coefs=TRUE,order.by.pip=FALSE)
#coefficients conditional on inclusion:
coef(mm,condi.coef=TRUE)
#same as
ests=coef(mm,condi.coef=FALSE)
ests[,2]/ests[,1]
#PIPs, coefficients, and signs based on the best 200 models
estimates.bma(mm,exact=TRUE)
#... and based on the 50 best models
coef(mm[1:50],exact=TRUE)
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