Nothing
# main function
bdw<-function(data , formula = NA ,
reg.q =FALSE , reg.b = FALSE ,
logit = TRUE ,
initial = c(.5,.5) , iteration = 25000 ,
v.scale = 0.1 ,
RJ = FALSE ,
dist.q = imp.d , dist.b = imp.d ,
q.par = c(0,0) , b.par = c(0,0) ,
penalized = FALSE ,
dist.l = imp.d , l.par = c(0,0) ,
bi.period =.25 , cov = 1 ,
sampling = c('bin'),
est = Mode ,
fixed.l= -1 ,
jeffrey = FALSE
)
{
jeffry = jeffrey
if (jeffry) {
dist.q = imp.d
dist.b = imp.d
dist.l = imp.d
}
#Rprof ( tf <- "nplregarma1.log", memory.profiling = TRUE )
chain = par.bayesian.tot.dw(data = data ,formula = formula ,
para.q = reg.q , para.b = reg.b ,
q.par = q.par , b.par = b.par ,
dist.q = dist.q , dist.b = dist.b ,
dist.l = dist.l , l.par = l.par ,
initials = initial , burn.in=bi.period ,
iterations = iteration , fixed.l = fixed.l ,
v.scale=v.scale , cov.m = cov ,
sampling = sampling , penalized = penalized,
logit = logit , RJ = RJ,
jeffry=jeffry
)
#Rprof ( NULL ) ; cat ( '\n ------- DEBUG DATA ------- \n' ) ; print ( summaryRprof ( tf ) )
res = apply(chain$chain,2,est)
out = list(res=res , chain=chain)
class(out) = 'bdw'
return(out)
}
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