coef.bma {BMS}  R Documentation 
Returns a matrix with aggregate covariatespecific Bayesian model Averaging: posterior inclusion probabilites (PIP), post. exepected values and standard deviations of coefficents, as well as sign probabilites
## S3 method for class 'bma' coef(object, exact = FALSE, order.by.pip = TRUE, include.constant = FALSE, incl.possign = TRUE, std.coefs = FALSE, condi.coef = FALSE, ...) #equivalent: estimates.bma(bmao, exact = FALSE, order.by.pip = TRUE, include.constant = FALSE, incl.possign = TRUE, std.coefs = FALSE, condi.coef = FALSE)
object, bmao 
a 'bma' object (cf. 
exact 
if 
order.by.pip 

include.constant 
If 
incl.possign 
If 
std.coefs 
If 
condi.coef 
If 
... 
further arguments for other 
More on the argument exact
:
In case the argument exact=TRUE
, the PIPs, coefficent statistics and conditional sign probabilities are computed on the basis of the (500) best models the sampling chain encoutered (cf. argument nmodel
in bms
). Here, the weights for Bayesian model averaging (BMA) are the posterior marginal likelihoods of these best models.
In case exact=FALSE
, then these statistics are based on all accepted models (except burnins): If mcmc="enumerate"
then this are simply all models of the traversed model space, with their marginal likelihoods providing the weights for BMA.
If, however, the bma object bmao
was based on an MCMC sampler (e.g. when bms
argument mcmc="bd"
), then BMA statistics are computed differently: In contrast to above, the weights for BMA are MCMC frequencies, i.e. how often the respective models were encountered by the MCMC sampler. (cf. a comparison of MCMC frequencies and marginal likelihoods for the best models via the function pmp.bma
).
A matrix with five columns (or four if incl.possign=FALSE
)
Column 'PIP' 
Posterior inclusion probabilities ∑ p(γi \in γ, Y) / sum p(γY) 
Column 'Post Mean' 
posterior expected value of coefficients, unconditional E(βY)=∑ p(γY) E(βγ,Y), where E(β_iγ,i \notin γ, Y)=0 if 
Column 'Post SD' 
posterior standard deviation of coefficients, unconditional or conditional on inclusion, depending on 
Column 'Cond.Pos.Sign' 
The ratio of how often the coefficients' expected values were positive conditional on inclusion. (over all visited models in case 
Column 'Idx' 
the original order of covariates as the were used for sampling. (if included, the constant has index 0) 
Martin Feldkircher and Stefan Zeugner
bms
for creating bma objects, pmp.bma
for comparing MCMC frequencies and marginal likelihoods.
Check http://bms.zeugner.eu for additional help.
#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 th emodels were drawn coef(mm) #standardized coefficents, ordered by index coef(mm,std.coefs=TRUE,order.by.pip=FALSE) #coefficents 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)