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library-bf-cens-conv-prior.R
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## install.packages(c("igraph0","BayesTree", "ggplot2","Hmisc","MCMCpack","LearnBayes","evd","plyr","dummies","bayespack"))
# install.packages(c("sp","numDeriv", "fields","rgl","mvtnorm","multicore","pixmap"))
library(MCMCpack)
library(LearnBayes)
library(evd)
library(plyr)
library(compiler)
library(parallel)
library(dummies)
library(survival)
#library(bayespack)
### Constants
l2p=log(2)+log(pi)
optimmethod=c("Nelder-Mead","CG")[1]
## Log Likelihoods and Kernel Posteriors with:
# b fraction (b=1 for usual)
# prior: unused
##Simulations from the generalized gumbel with k=2
rggumbel=function(nrep,nosirve1=0,nosirve2=1){
ts=rgamma(nrep,shape=2,rate=1)
return(ws=log(ts))
}
qggumbel=function(pcens,nosirve1=0,nosirve2=1) return(log(qgamma(pcens,shape=2,rate=1)))
# Log-Generalized Gamma k=2
###################################
log.post.ggamma.nc=function (parms, Data,b,prior=1){
z = (Data[,1] - cbind(Data[,-(1:2)]) %*% parms[-1])/exp(parms[1])
log.f = -parms[1]+2*z-exp(z)
log.S=-exp(z)+log(1+exp(z))
return(sum(Data[,2] * log.f*b[1] + (1 - Data[,2]) * log.S*b[2]))
}
log.prior.cp.gprior=function(parms,nunc,XtX.inv){
np=length(parms)
res=0
if(np>2) res=dmnorm(x=parms[-c(1,2)],mean=0,
varcov=exp(2*parms[1])*nunc*XtX.inv,log=TRUE)
return(res)
}
log.prior.cp.zs=function(parms,nunc,XtX.inv){
np=length(parms)
res=0
if(np>2) res=dmt(x=parms[-c(1,2)],
S=exp(2*parms[1])*nunc*XtX.inv,df=1,log=TRUE)
return(res)
}
# Log-Normal with Conventional Prior
log.post.lnormal.cp.nc=function (parms, Data,b,prior=1,XtX.inv=NULL,nunc=NULL){
z = (Data[,1] - cbind(Data[,-(1:2)]) %*% parms[-1])/exp(parms[1])
log.f = -parms[1]-0.5*z^2
log.S=pnorm(z,lower.tail=FALSE,log.p=TRUE)
return(log.prior.cp(parms,nunc,XtX.inv)+sum(Data[,2] * log.f + (1 - Data[,2]) * log.S))
}
# Log-Normal
log.post.lnormal.nc=function (parms, Data,b,prior=1,XtX.inv=NULL,nunc=NULL){
z = (Data[,1] - cbind(Data[,-(1:2)]) %*% parms[-1])/exp(parms[1])
log.f = -parms[1]-0.5*z^2
log.S=pnorm(z,lower.tail=FALSE,log.p=TRUE)
return(sum(Data[,2] * log.f*b[1] + (1 - Data[,2]) * log.S*b[2]))
}
# Weibull
log.post.weibull.nc=function (parms, Data,b,prior=1,XtX.inv=NULL,nunc=NULL){
z = (Data[,1] - cbind(Data[,-(1:2)]) %*% parms[-1])/exp(parms[1])
log.f = -parms[1] +(z - exp(z))
log.S = -exp(z)
return(sum(Data[,2] * log.f*b[1] + (1 - Data[,2]) * log.S*b[2]))
}
log.post.weibull=cmpfun(log.post.weibull.nc)
log.post.lnormal=cmpfun(log.post.lnormal.nc)
log.post.lnormal.cp=cmpfun(log.post.lnormal.cp.nc)
log.post.ggamma=cmpfun(log.post.ggamma.nc)
############# Posterior Calculation using MCMC
post=function(log.post,nsimul,parms.init,b,dat,nunc,XtX.inv){
fit=optim(parms.init,log.post,b=b,Data=dat,XtX.inv=XtX.inv,nunc=nunc,control=list(fnscale=-1),method=optimmethod,hessian=TRUE)
var.prop=-solve(fit$hessian)
proposal=list(var=var.prop,scale=2)
bayesfit=rwmetrop(log.post,proposal,fit$par,nsimul,b=b,Data=dat,XtX.inv=XtX.inv,nunc=nunc)
stuff=list(var.prop=(proposal$scale^2)*proposal$var,par=bayesfit$par)
return(stuff)
}
# Calculation of log Marginal di Chibb-Jeliazkov
log.marg.nc=function (log.post, parms.hat, par, var.prop, dat, b,nunc,XtX.inv){
nsimul=nrow(par)
#calcolo della posterior approssimata
lbc = log.post(parms.hat, dat, b,XtX.inv=XtX.inv,nunc=nunc)
lb=aaply(par,1,log.post,Data=dat,b=b,XtX.inv=XtX.inv,nunc=nunc)
num=mean(exp(lbc-lb+dmnorm(par,parms.hat,var.prop,log=T)))
parms.j=mvrnorm(nsimul,mu=parms.hat,Sigma=var.prop)
lbc.den = aaply(parms.j,1,log.post,Data=dat,b=b,XtX.inv=XtX.inv,nunc=nunc)
lb.den = lbc
den=mean(exp(lbc.den-lb.den))
return(log.post(parms.hat,Data=dat,b=b,XtX.inv=XtX.inv,nunc=nunc)-log(num)+log(den))
}
log.marg=cmpfun(log.marg.nc)
# Calculation of log Marginal Laplace using banint of package BayesPack
log.marg.laplace.bp=function (log.post, parms.hat, dat, b,XtX.inv,nunc){
mlp=function(parms) log.post(parms,Data=dat,b=b,XtX.inv=XtX.inv,nunc=nunc)
res=banint(mlp,mode=parms.hat,optimMethod = optimmethod,method="Monte Carlo")$nrmcon[1]
return(res)
}
# Calculation of log Marginal Laplace
log.marg.laplace.nc=function (log.post, parms.hat, dat, b,XtX.inv,nunc){
d=length(parms.hat)
N=nrow(dat)
fit=optim(parms.hat,log.post,b=b,Data=dat,XtX.inv=XtX.inv,nunc=nunc,control=list(fnscale=-1),method=optimmethod,hessian=TRUE)
M=fit$value+l2p*d*0.5-log(abs(det(fit$hessian)))*0.5
return(M)
}
log.marg.laplace=log.marg.laplace.nc
nunc.func.nu=function(x) sum(x)
nunc.func.n=function(x) length(x)
nunc.func.uno=function(x) 1
func.XtX.inv.n=function(rel,XM,y=NULL,model=NULL) solve(t(XM)%*%XM)
func.XtX.inv.nu=function(rel,XM,y=NULL,model=NULL) solve(t(XM[rel==1,])%*%XM[rel==1,])
func.XtX.obsfisher.old=function(rel,XM,y=NULL){
tt=survreg(Surv(exp(y),rel)~ XM,dist="lognormal")
SIG=tt$var[-c(1,nrow(tt$var)),-c(1,nrow(tt$var))]/tt$scale^2
return(SIG)
}
func.XtX.obsfisher=function(rel,XM,y=NULL){
tt=survreg(Surv(exp(y),rel)~ XM,dist="lognormal")
ic=rel==0
iu=!ic
XMw.u=iu*XM
XtX.uw=t(XMw.u)%*%XMw.u
resid=c((y-tt$coef%*%t(cbind(rep(1,n),XM)))/tt$scale)
ww=sqrt(2*pi)*(1-pnorm(resid))
ww=1/ww*(exp(-resid^2)/ww-resid*exp(-resid^2/2))*ic
XMw.c=sqrt(ww)*XM
XtX.cw=t(XMw.c)%*%XMw.c
I.F=XtX.uw/sum(iu)+XtX.cw/sum(ic)
SIG=solve(I.F)
return(SIG)
}
############# Log of BF 1 against 0 based on data0 and data1
log.bf.parziale=function(data0,data1,nsimul,parms.init.used,b,log.post,use.MCMC=NULL){
parms.hat0=parms.init.used[[1]]
parms.hat1=parms.init.used[[2]]
nunc=nunc.func(data1[,2])
XtX.inv=func.XtX.inv(rel=data1[,2],XM=cbind(data1[,-(1:3)]),y=data1[,1])
if(use.MCMC){ ## Use MCMC
res=post(log.post,nsimul,parms.hat0,dat=data0,b=b,XtX.inv=NULL,nunc=NULL)
res$par=res$par[-(1:(nsimul*0.3)),]
parms.hat0=aaply(res$par,2,median)
log.marg0=log.marg(log.post,parms.hat0,res$par,res$var.prop,dat=data0,b=b)
res=post(log.post,nsimul,parms.hat1,dat=data1,b=b,nunc=nunc,XtX.inv=XtX.inv)
res$par=res$par[-(1:(nsimul*0.3)),]
parms.hat1=aaply(res$par,2,median)
log.marg1=log.marg(log.post,parms.hat1,res$par,res$var.prop,
dat=data1,b=b,XtX.inv=XtX.inv,nunc=nunc)
}
#log.marg1-log.marg0<0
if(use.MCMC==FALSE){ ## Use Laplace
log.marg0=log.marg.laplace(log.post=log.post,
parms.hat=parms.hat0,dat=data0,b=b,XtX.inv=NULL,nunc=NULL)
log.marg1=log.marg.laplace(log.post=log.post,
parms.hat=parms.hat1,dat=data1,b=b,XtX.inv=XtX.inv,nunc=nunc)
}
return(log.marg1-log.marg0)
}
############# Data Simulation according to the model specified
sim.dati=function(n,beta.true,p.cens = 0.3,sigma,mu,model="lnorm"){
parms.true = c(log(sigma), mu, beta.true)
p = length(beta.true)
ones = rep(1,n)
x = cbind(ones, array(rnorm(n*p,sd=3), dim=c(n,p)))
#if(p==1) x[,2]=x[,2]-mean(x[,2]) else x[,-1]=apply(x[,-1],2,function(xx) xx-mean(xx))
mu1=x%*%parms.true[-1]
if(model=="lnorm"){
rmodel=rnorm
mu2 = mu1-sqrt(2*sigma^2)*qnorm(p.cens,0,1)
}
if(model=="weibull"){
rmodel=rgumbel
lambda = exp(-(mu1)/exp(parms.true[1]))
alpha = 1/exp(parms.true[1])
rate.weib = lambda*p.cens/(1-p.cens)
}
if(model=="ggamma"){
rmodel=rggumbel
mu2 = mu1-qggumbel(p.cens,0,1)
}
if((model=="weibull")|(model=="lnorm")) y = mu1-exp(parms.true[1])*rmodel(n,0,1)
if(model=="ggamma") y = mu1+exp(parms.true[1])*rmodel(n,0,1)
## Cens Part
ncens = round(n*p.cens,0)
relapse = ones
ids = rep(FALSE,n)
while(sum(ids)<ncens){
if(model=="weibull") censtimes = log(rweibull(n, shape=alpha, scale=(1/rate.weib)^(1/alpha)))
if(model=="lnorm") censtimes = rnorm(n,mu2,sigma)
if(model=="ggamma") censtimes = mu2+rmodel(n,0,1)
ids = censtimes<y
}
if(sum(ids)>0){
#ind.cens = sample(which(ids), ncens)
#y[ind.cens] = censtimes[ind.cens]
y[ids]=censtimes[ids]
relapse[ids] = 0
}
dat=cbind(y,relapse,x)
parms.init = list(init0=parms.true[c(1,2)], init1=parms.true)
return(list(dat=dat,parms.init=parms.init,censtimes=censtimes))
}
#sim.dati.detcens(n,beta.true,p.cens,sigma=sigma.true,mu=mu.true,model=model)
############# Data Simulation according to the model specified
sim.dati.detcens=function(n,beta.true,p.cens = 0.3,sigma,mu,model="lnorm"){
parms.true = c(log(sigma), mu, beta.true)
p = length(beta.true)
ones = rep(1,n)
x = cbind(ones, array(rnorm(n*p,sd=3), dim=c(n,p)))
if(p==1) x[,2]=x[,2]-mean(x[,2]) else x[,-1]=apply(x[,-1],2,function(xx) xx-mean(xx))
mu1=x%*%parms.true[-1]
ind.ord=sort(mu1,index.return=TRUE)$ix
mu1=mu1[ind.ord]
x=x[ind.ord,]
ncens = round(n*p.cens,0)
id.cens=c(1:(ncens/2),floor((n-ncens/2+1)):n)
p.cens2=0.99
p.cens3=0.01
mu2=rep(NA,length(mu1))
if(model=="lnorm"){
rmodel=rnorm
mu2[id.cens]=mu1[id.cens]-sqrt(2*sigma^2)*qnorm(p.cens2,0,1)
mu2[-id.cens] = mu1[-id.cens]-sqrt(2*sigma^2)*qnorm(p.cens3,0,1)
}
if(model=="weibull"){
rmodel=rgumbel
lambda = exp(-(mu1)/exp(parms.true[1]))
alpha = 1/exp(parms.true[1])
rate.weib = lambda*p.cens2/(1-p.cens2)
}
if(model=="ggamma"){
rmodel=rggumbel
mu2 = mu1-qggumbel(p.cens2,0,1)
}
if((model=="weibull")|(model=="lnorm")) y = mu1-exp(parms.true[1])*rmodel(n,0,1)
if(model=="ggamma") y = mu1+exp(parms.true[1])*rmodel(n,0,1)
## Cens Part
relapse = ones
ids = rep(FALSE,n)
if(model=="weibull") censtimes = log(rweibull(n, shape=alpha, scale=(1/rate.weib)^(1/alpha)))
if(model=="lnorm") censtimes = rnorm(n,mu2,sigma)
if(model=="ggamma") censtimes = mu2+rmodel(n,0,1)
ids = censtimes<y
relapse[ids] = 0
y[ids] = censtimes[ids]
dat=cbind(y,relapse,x)
parms.init = list(init0=parms.true[c(1,2)], init1=parms.true)
return(list(dat=dat,parms.init=parms.init,censtimes=censtimes))
}
############# Data Simulation according to the model specified
sim.dati.detcens.mahala=function(n,beta.true,p.cens = 0.3,sigma,mu,model="lnorm"){
parms.true = c(log(sigma), mu, beta.true)
p = length(beta.true)
ones = rep(1,n)
x = cbind(ones, array(rnorm(n*p,sd=3), dim=c(n,p)))
if(p==1){ x[,2]=x[,2]-mean(x[,2])} else{x[,-1]=apply(x[,-1],2,function(xx) xx-mean(xx))}
if(sum(beta.true)>0){
covari=as.matrix(x[,-1])
var.x=var(covari[,which(beta.true!=0)])
maha.dist=mahalanobis(as.matrix(covari[,which(beta.true!=0)],nrow=n), rep(0,sum(beta.true)), cov=var.x)
ind.ord=sort(maha.dist,index.return=TRUE)$ix}else{ind.ord=1:n}
mu1=x%*%parms.true[-1]
mu1=mu1[ind.ord]
x=x[ind.ord,]
ncens = round(n*p.cens,0)
id.cens=(n-ncens+1):n
if(model=="lnorm"){
rmodel=rnorm
mu2 = mu1-sqrt(2*sigma^2)*qnorm(p.cens,0,1)
}
if(model=="weibull"){
rmodel=rgumbel
lambda = exp(-(mu1)/exp(parms.true[1]))
alpha = 1/exp(parms.true[1])
rate.weib = lambda*p.cens2/(1-p.cens2)
}
if(model=="ggamma"){
rmodel=rggumbel
mu2 = mu1-qggumbel(p.cens2,0,1)
}
if((model=="weibull")|(model=="lnorm")) y = mu1-exp(parms.true[1])*rmodel(n,0,1)
if(model=="ggamma") y = mu1+exp(parms.true[1])*rmodel(n,0,1)
## Cens Part
relapse = ones
ids = rep(FALSE,n)
if(model=="weibull") censtimes = log(rweibull(n, shape=alpha, scale=(1/rate.weib)^(1/alpha)))
if(model=="lnorm") censtimes = rnorm(n,mu2,sigma)
if(model=="ggamma") censtimes = mu2+rmodel(n,0,1)
#ids = censtimes<y
ids=id.cens
relapse[ids] = 0
y[ids] = censtimes[ids]
dat=cbind(y,relapse,x)
parms.init = list(init0=parms.true[c(1,2)], init1=parms.true)
return(list(dat=dat,parms.init=parms.init,censtimes=censtimes))
}
############# Data Simulation according to the model specified
sim.dati.known.cens=function(n,beta.true,p.cens = 0.3,sigma,mu,model="lnorm"){
parms.true = c(log(sigma), mu, beta.true)
p = length(beta.true)
ones = rep(1,n)
x = cbind(ones, array(rnorm(n*p,sd=3), dim=c(n,p)))
if(p==1) x[,2]=x[,2]-mean(x[,2]) else x[,-1]=apply(x[,-1],2,function(xx) xx-mean(xx))
mu1=x%*%parms.true[-1]
if(model=="lnorm"){
rmodel=rnorm
mu2 = mu1-sqrt(2*sigma^2)*qnorm(p.cens,0,1)
}
if(model=="weibull"){
rmodel=rgumbel
lambda = exp(-(mu1)/exp(parms.true[1]))
alpha = 1/exp(parms.true[1])
rate.weib = lambda*p.cens/(1-p.cens)
}
if(model=="ggamma"){
rmodel=rggumbel
mu2 = mu1-qggumbel(p.cens,0,1)
}
if((model=="weibull")|(model=="lnorm")) y = mu1-exp(parms.true[1])*rmodel(n,0,1)
if(model=="ggamma") y = mu1+exp(parms.true[1])*rmodel(n,0,1)
## Cens Part
ncens = round(n*p.cens,0)
relapse = ones
ids = rep(FALSE,n)
if(model=="weibull") censtimes = log(rweibull(n, shape=alpha, scale=(1/rate.weib)^(1/alpha)))
if(model=="lnorm") censtimes = rnorm(n,mu2,sigma)
if(model=="ggamma") censtimes = mu2+rmodel(n,0,1)
ids = censtimes<y
y[ids] = censtimes[ids]
relapse[ids] = 0
dat=cbind(y,relapse,x)
parms.init = list(init0=parms.true[c(1,2)], init1=parms.true)
return(list(dat=dat,parms.init=parms.init,censtimes=censtimes))
}
############# Calculate the model indices based on only additive effects of the covariates
index.models=function(numvar,onlyone=FALSE){
indici = seq(1,numvar)
ind.betas = NULL
for(j in 1:(numvar*(1-onlyone)+1*(onlyone))){
comb.beta = combn(indici, j)
for(i in 1:dim(comb.beta)[2]) ind.betas=c(ind.betas,list(comb.beta[,i]))
}
return(ind.betas)
}
############# Probability distribution of Nt
prob.nstar=function(nstar,n,ncens,p) exp(lchoose(ncens,nstar-p)+lchoose(n-ncens-1,p-1)+lfactorial(nstar-1)+log(n-ncens)-(lfactorial(n)-lfactorial(n-nstar)))
# Probability distribution of B:
# use="all" : all B and probabilities;
# use="mode" : mode of B
# use="median" : median of B
prob.b=function(n,ncens,p,use,toplot=FALSE){
nstar.possible=seq(p,ncens+p)
nstar.dens=aaply(nstar.possible,1,prob.nstar,n=n,ncens=ncens,p=p)
if(toplot) barplot(height=nstar.dens,names.arg=nstar.possible,xlab="nt",ylab="de",main="")
if(use=="all"){
b.frac=cbind(p/(n-ncens),(nstar.possible-p)/ncens)
b.frac.prob=nstar.dens}
if(use=="mode"){
nstar.mode=nstar.possible[nstar.dens==max(nstar.dens)][1]
b.frac.prob=1
b.frac=cbind(p/(n-ncens),(nstar.mode-p)/ncens)}
if(use=="median"){
nstar.median=min(nstar.possible[cumsum(nstar.dens)>=0.5])[1]
b.frac.prob=1
b.frac=cbind(p/(n-ncens),(nstar.median-p)/ncens)}
return(list(B=b.frac,B.prob=b.frac.prob))
}
############# Return the indices for a Training sample
training.sample.conv.prior=function(dati,num.par=2){
n=nrow(dati)
cens=dati[,2]
#spazio campionario
camp=1:n
ind.train=NULL
while(sum(cens[ind.train])<num.par){
ind.train=c(ind.train,sample(camp,size=1))
camp=camp[!(camp%in%ind.train)]
}
return(ind.train)
}
######## Bayes Factors ########
# Fractional Bayes Factor with specified fractions Bs and probabiities
# for a certain model specified in ind.beta
fbf.ind.beta=function(data0,data1,Bs,Bmode,use.pericchi2005.fbf=FALSE,
parms.init.used,nsimul,log.post,force.L=NULL,use.MCMC=NULL,log.bf.all=NULL){
B=Bs$B
B.prob=Bs$B.prob
if(use.pericchi2005.fbf){
tmp=aaply(B,1,function(x){
log.marg0=log.post(parms=parms.init.used[[1]],Data=data0,b=1-x)
log.marg1=log.post(parms=parms.init.used[[2]],Data=data1,b=1-x)
m1=length(parms.init.used[[2]])
m0=length(parms.init.used[[1]])
return( (log.marg1-log.marg0)+0.5*(m1-m0)*log(x) )})
}else{
tmp=aaply(B,1,function(x) log.bf.parziale(data0,data1,nsimul,parms.init.used=parms.init.used,
b=x,log.post=log.post,use.MCMC=use.MCMC))
}
a=(1-use.pericchi2005.fbf)
b=c(-1,1)[1+use.pericchi2005.fbf]
resp=exp(log.bf.all*a+b*tmp)
nvalid=(!(abs(resp)<Inf))|is.na(resp)
resp[nvalid]=sign(resp[nvalid])*2*max(resp[!nvalid],na.rm=TRUE)
mfbf=log(sum(resp*B.prob))
whichmode=B.prob==max(B.prob)
fbfmode=log(resp[whichmode])
names(mfbf)=names(fbfmode)=NULL
return(list(mfbf=mfbf,fbfmode=fbfmode))
}
fbf.ind.betalnorm=function(data0,data1,Bs,Bmode,use.pericchi2005.fbf=FALSE,
parms.init.used,nsimul,log.post,force.L=NULL,use.MCMC=NULL,log.bf.all=NULL){
B=Bs$B
B.prob=Bs$B.prob
tmp=aaply(B,1,function(x){
Ri=lm(y~1,data=data.frame(data0))
Ri=sum(Ri$residuals^2)
Rj=lm(y~.,data=data.frame(data1[,-2]))
Rj=sum(Rj$residuals^2)
b=x
kj=ncol(data1)-2
ki=ncol(data0)-2
qj=0
qi=0
r=b*nrow(data0)
lbfb=(lgamma(0.5*(r-ki))+
lgamma(0.5*(n-kj))-lgamma(0.5*(r-kj))-
lgamma(0.5*(n-ki))+(n-r)*0.5*(log(Ri)-log(Rj)))
return(lbfb)})
mfbf=log(sum(exp(tmp)*B.prob))
fbfmode=tmp[B==Bmode$B]
names(mfbf)=names(fbfmode)=NULL
return(list(mfbf=mfbf,fbfmode=fbfmode))
}
# Intrindic Bayes Factor with specified fractions Bs and probabiities
# for a certain model specified in ind.beta
# force.L : if not null force to use exactly L training sample
ibf.ind.beta=function(data0,data1,Bs,parms.init.used,nsimul,log.post,use.pericchi2005.fbf=NULL,
force.L=NULL,use.MCMC=NULL,Bmode=NULL,log.bf.all=NULL){
if(is.null(force.L)){L=round(Bs[[1]]$B[1]*nrow(data0)^2,0)}else{L=force.L}
tmp=aaply(1:L,1,function(x){
lbfl="err"
while(is.character(lbfl)){
ts=training.sample(data1)
lbfl=try(log.bf.parziale(data0[ts,],data1[ts,],nsimul,parms.init.used,
b=c(1,1),log.post,use.MCMC=use.MCMC),silent=TRUE)
}
return(lbfl)})
mibf=log.bf.all-median(tmp,na.rm=TRUE)
etmp=exp(-tmp)
etmp=etmp[etmp<Inf]
aibf=log.bf.all+log(mean(etmp,na.rm=TRUE))
names(mibf)=names(aibf)=NULL
return(list(mibf=mibf,aibf=aibf))
}
# Return the indices for a Kaplan-Meieir Training sample
mts.kaplan.meier = function(mydata,p)
{
# Data set under the null
mydata = mydata[order(mydata[,1]),] # Sort data (same order as surv$time)
# KM Estimator
mysurv = survfit(Surv(exp(mydata[,1]), mydata[,2]) ~ 1,type="kaplan-meier")
n = nrow(mydata) # Sample size
mts.size = p # MTS Sample Size
probs = 1-mysurv$surv # CDF
probs[n] = 1 # Adjusted CDF at the maximum
probs = c(probs[1],probs[-1]-probs[-n]) # Point mass at each Observations
not.censored.obs = which(mydata[,2]==1) # Not censored Obs
elle = sample(x=not.censored.obs,size=mts.size,prob=probs[not.censored.obs])
# The elle
return(elle)
}
lgamma05=lgamma(0.5)
log.bf.parzialekm=function(data0,data1){
k1 = dim(data1)[2]-2
k0 = dim(data0)[2]-2
r = k1-k0
n = dim(data0)[1]
t.med = sum(data0[,1])/n
X1 = data1[,-c(1,2)]
X0 = data0[,-c(1,2)]
inv = solve(t(X1)%*%X1)
beta1.hat = (inv%*%t(X1)%*%data1[,1])
R0=data0[,1]-t.med
R0 = c( t(R0)%*%(R0))
R1=data1[,1]-X1%*%beta1.hat
R1 = c( t(R1) %*% (R1))
# BF i contro 0
ibf.N.l = k1*lgamma05+lgamma((n-k1)*0.5) +0.5*log(det(t(X0)%*%X0))+((n-k0)*0.5)*log(R0)-(k0*lgamma05+lgamma((n-k0)*0.5)+0.5*log(det(t(X1)%*%X1))+((n-k1)*0.5)*log(R1) )
return(ibf.N.l)
}
# Intrindic Bayes Factor KM (normal only) with specified fractions Bs and probabiities
# for a certain model specified in ind.beta
# force.L : if not null force to use exactly L training sample
ibfkm.ind.beta=function(data0,data1,Bs,parms.init.used,nsimul,log.post,use.pericchi2005.fbf=NULL,
force.L=NULL,use.MCMC=NULL,Bmode=NULL,log.bf.all=NULL){
if(is.null(force.L)){L=round(Bs[[1]]$B[1]*nrow(data0)^2,0)}else{L=force.L}
tmp=aaply(1:L,1,function(x){
lbfl="err"
while(is.character(lbfl)){
ts=mts.kaplan.meier(data0,p=ncol(data1)-1)
lbfl=try(log.bf.parzialekm(data0[ts,],data1[ts,]),silent=TRUE)
}
return(lbfl)})
mibf=log.bf.all-median(tmp,na.rm=TRUE)
etmp=exp(-tmp)
etmp=etmp[etmp<Inf]
aibf=log.bf.all+log(mean(etmp,na.rm=TRUE))
names(mibf)=names(aibf)=NULL
return(list(mibf=mibf,aibf=aibf))
}
# BIC with specified fractions Bs and probabiities
# for a certain model specified in ind.beta
bic.ind.beta=function(data0,data1,Bs,parms.init.used,nsimul,log.post,force.L=NULL,use.pericchi2005.fbf=NULL,
use.MCMC=NULL,Bmode=NULL,log.bf.all=NULL){
n.unc=sum(data0[,2])
log0=log.post(parms=parms.init.used[[1]],Data=data0,b=c(1,1))
log1=log.post(parms=parms.init.used[[2]],Data=data1,b=c(1,1))
m0=length(parms.init.used[[1]])
m1=length(parms.init.used[[2]])
resbic=log1-log0+0.5*(m0-m1)*log(n.unc)
names(resbic)=NULL
return(resbic)
}
mppm.func=function(probs,ind.betas,nmodels=NULL){
covars=1:max(unlist(ind.betas))
if(is.null(nmodels)) nmodels=length(ind.betas)
prob.covars=covars*0
for(i in 1:nmodels) prob.covars[ind.betas[[i]]]=prob.covars[ind.betas[[i]]]+probs[i+1]
if(any(prob.covars>0.5)){
tt=which(prob.covars>0.5)
res=which(laply(ind.betas,function(x) all(x%in%tt)&all(tt%in%x)))
}else{res=0}
return(res+1)
}
######## Main Function ########
# Calculate the BF, specified in typebf for all models.
# Return:
# - log of BF of 1 against the null;
# - model posterior probability;
# - HPPM model;
# - MPPM model;
# - Mean model size
# - Posterior Expected Model Size
allbf=function(ind.betas,dat,Bs,Bmode,parms.init,nsimul,use.pericchi2005.fbf,
nprocess,log.post,typebf,force.L=NULL,use.MCMC=TRUE,log.bf.all=NULL,true.sim=NULL,
use.true.sim=FALSE,logbf=NULL,no.bergscott.prior){
nmodels=length(ind.betas)
if(is.null(logbf)){
tmp=mclapply(as.list(1:nmodels),function(ii){
data0=as.matrix(cbind(dat[1:2],1))
data1=as.matrix(cbind(dat[1:2],model.matrix(~.,data=dat[ind.betas[[ii]]+2])))
typebf(data0,data1,parms.init.used=list(parms.init[[1]],parms.init[[ii+1]]),
nsimul=nsimul,log.post=log.post,Bs=Bs[[ii]],Bmode=Bmode[[ii]],
use.pericchi2005.fbf=use.pericchi2005.fbf,
force.L=force.L,use.MCMC=use.MCMC,log.bf.all=log.bf.all[ii])},
mc.cores=nprocess)
tmp=unlist(tmp)
tmp=matrix(tmp,nmodels,length(tmp)/nmodels,byrow=TRUE,dimnames=list(NULL,names(tmp)[1:(length(tmp)/nmodels)]))
logbf=NULL
for(i in 1:ncol(tmp)) logbf=cbind(logbf,unlist(tmp[,i]))
logbf=rbind(rep(0,ncol(tmp)),logbf)
colnames(logbf)=colnames(tmp)
}
rownames(logbf)=c("0",paste(ind.betas))
model.post=function(x,no.bergscott.prior){
if(no.bergscott.prior){
res=exp(x)/sum(exp(x))}else{
ncov=c(0,laply(ind.betas,length))
uncov=unique(ncov)
p=max(ncov)
redprior=1/(p+1)
redprior=redprior/c(table(ncov))
modprior=redprior[ncov+1]
res=(exp(x)*modprior)/sum(exp(x)*modprior)
}
return(res)
}
ppost=cbind(aaply(logbf,2,model.post,no.bergscott.prior=no.bergscott.prior))
if(nrow(ppost)<ncol(ppost)) ppost=t(ppost)
if(ncol(ppost)>1){
hppm.num=aaply(ppost,2,function(x) which(x==max(x))[1])}else{
hppm.num=which(ppost==max(ppost))}
if(ncol(ppost)>1){mppm.num=aaply(ppost,2,mppm.func,ind.betas=ind.betas,nmodels=nmodels)}else{
mppm.num=mppm.func(ppost,ind.betas,nmodels)}
hppm=aaply(hppm.num,1,function(x) rownames(logbf)[x])
mppm=aaply(mppm.num,1,function(x) rownames(logbf)[x])
pems=c(0,laply(ind.betas,function(x) length(x)))
if(ncol(ppost)>1){pems=t(ppost)%*%pems}else{pems=sum(ppost*pems)}
name.cov=colnames(dat)[-(1:2)]
rownames(logbf)=c("*NULL*",aaply(rownames(logbf)[-1],1,function(x) paste(name.cov[eval(parse(text=x))],collapse="-")))
rownames(ppost)=c("*NULL*",aaply(rownames(ppost)[-1],1,function(x) paste(name.cov[eval(parse(text=x))],collapse="-")))
hppm=laply(hppm,function(x) paste(name.cov[eval(parse(text=x))],collapse="-"))
hppm[hppm==""]="*NULL*"
mppm=laply(mppm,function(x) paste(name.cov[eval(parse(text=x))],collapse="-"))
mppm[mppm==""]="*NULL*"
if(length(hppm)>1) names(hppm)=names(mppm)=colnames(tmp)
if(is.null(true.sim)){
res.allbf=list(logbf=logbf,ppost=ppost,hppm=hppm,mppm=mppm,pems=pems)}else{
ppost=as.matrix(ppost)
ppost.true=ppost[true.sim,]
if(length(ppost.true)==1) names(ppost.true)=NULL
pems=as.matrix(pems)
names(pems)=names(ppost.true)
hppm.succ=hppm.num==true.sim
mppm.succ=mppm.num==true.sim
names(mppm.succ)=NULL
res.allbf=list(ppost=ppost.true,hppm=hppm.succ,mppm=mppm.succ,pems=pems)
}
return(res.allbf)
}
analyse.data=function(datos,calc.cpbf=TRUE,calc.bic=TRUE,calc.fbf=TRUE,calc.fbfmode=TRUE,use.pericchi2005.fbf=TRUE,
calc.ibf=TRUE,nsimul=1000,force.L=TRUE,useB="Bmode",useB.ibf="Bmode",
use.Bsuni=FALSE,use.MCMC=FALSE,model,nprocess,true.sim=NULL,use.true.sim=FALSE,onlyone=FALSE,
no.bergscott.prior=TRUE,calc.ibfkm=FALSE){
if(lmodel=="log.post.gamma") log.post=log.post.ggamma
if(lmodel=="log.post.lnormal") log.post=log.post.lnormal
if(lmodel=="log.post.weibull") log.post=log.post.weibull
if(lmodel=="log.post.lnormal.cp") log.post=log.post.lnormal.cp
# Model Index
numcov=ncol(datos)-2
ind.betas=index.models(numcov,onlyone=onlyone)
n=nrow(datos)
#if(use.true.sim){
beta.trues=llply(ind.betas,function(x){
rr=rep(0,numcov)
rr[x]=1
return(rr)})
#}
# Parms init
data0=as.matrix(cbind(datos[1:2],1))
if(use.true.sim){tt1=c(0,0)}else{
tt1=optim(par=c(0,0),fn=log.post,control=list(fnscale=-1),Data=data0,b=c(1,1),method=optimmethod)$par
}
tmpmyf=function(ii){
data1=as.matrix(cbind(datos[1:2],model.matrix(~.,data=datos[ind.betas[[ii]]+2])))
if(use.true.sim){pars=c(tt1,unlist(beta.trues[ii])[ind.betas[[ii]]])
logbf=log.post(parms=pars,Data=data1,b=c(1,1))-log.post(parms=tt1,Data=data0,b=c(1,1))
}else{
nunc=nunc.func(data1[,2])
XtX.inv=func.XtX.inv(rel=data1[,2],XM=cbind(data1[,-(1:3)]),y=data1[,1])
pars=optim(par=c(rep(0,2),unlist(beta.trues[ii])[ind.betas[[ii]]]),fn=log.post,control=list(fnscale=-1),Data=data1,b=c(1,1),nunc=nunc,XtX.inv=XtX.inv,method=optimmethod)$par
logbf=log.bf.parziale(data0,data1,nsimul,parms.init.used=list(tt1,pars),b=c(1,1),log.post=log.post,use.MCMC=use.MCMC)
}
return(list(pars=pars,logbf=logbf))}
ltt=alply(1:length(ind.betas),1,tmpmyf,.parallel=TRUE)
parms.init=c(list(tt1))
log.bf.all=NULL
for(i in 1:length(ind.betas)){
parms.init=c(parms.init,ltt[[i]][1])
log.bf.all=c(log.bf.all,ltt[[i]][2])
}
log.bf.all=unlist(log.bf.all)
nummaxpar=max(unlist(llply(ind.betas,length)))+2
# Calculates of Fraction B
if(use.Bsuni){
# All same B and same L
allB=llply(ind.betas,function(x) prob.b(n=n,ncens=n-sum(datos[,2]),p=nummaxpar,use="all"))
Bmode=llply(ind.betas,function(x) prob.b(n=n,ncens=n-sum(datos[,2]),p=nummaxpar,use="mode"))
#Bmedian=llply(ind.betas,function(x) prob.b(n=n,ncens=n-sum(datos[,2]),p=numcov+2,use="median"))
}else{
# Different B and Different L
allB=llply(ind.betas,function(x) prob.b(n=n,ncens=n-sum(datos[,2]),p=length(x)+2,use="all"))
Bmode=llply(ind.betas,function(x) prob.b(n=n,ncens=n-sum(datos[,2]),p=length(x)+2,use="mode"))
#Bmedian=llply(ind.betas,function(x) prob.b(n=n,ncens=n-sum(datos[,2]),p=length(x)+2,use="median"))
}
Bs=get(useB)
#BF with Conventional Prior:
if(calc.cpbf){
res.cpbf=allbf(ind.betas=ind.betas,dat=datos,Bs=Bs,Bmode=Bmode,parms.init=parms.init,
nsimul=nsimul,nprocess=nprocess,log.post=log.post,use.pericchi2005.fbf=NULL,
typebf=NULL,use.MCMC=NULL,true.sim=true.sim,no.bergscott.prior=no.bergscott.prior,logbf=t(t(c(0,log.bf.all))))
}else{res.cpbf=NULL}
# Fractional BF
if(calc.fbf){
res.fbf=allbf(ind.betas=ind.betas,dat=datos,Bs=Bs,Bmode=Bmode,parms.init=parms.init,
nsimul=nsimul,nprocess=nprocess,log.post=log.post,use.pericchi2005.fbf=use.pericchi2005.fbf,
typebf=fbf.ind.beta,use.MCMC=use.MCMC,log.bf.all=log.bf.all,true.sim=true.sim,
use.true.sim=use.true.sim,no.bergscott.prior=no.bergscott.prior)
}else{res.fbf=NULL}
# Intrinsic BF
if(calc.ibf){
num.max.fractions=NULL
if(force.L) num.max.fractions=max(aaply(1:length(ind.betas),.margins=1,.fun=function(i) length(allB[[i]]$B[,1])))
Bs=get(useB.ibf)
res.ibf=allbf(ind.betas=ind.betas,dat=datos,Bs=Bs,Bmode=Bmode,parms.init=parms.init,
nsimul=nsimul,nprocess=nprocess,log.post=log.post,use.pericchi2005.fbf=NULL,
typebf=ibf.ind.beta,force.L=num.max.fractions,use.MCMC=use.MCMC,
log.bf.all=log.bf.all,true.sim=true.sim,no.bergscott.prior=no.bergscott.prior)
}else{res.ibf=NULL}
# KM Intrinsic BF
if(calc.ibfkm){
num.max.fractions=NULL
if(force.L) num.max.fractions=max(aaply(1:length(ind.betas),.margins=1,.fun=function(i) length(allB[[i]]$B[,1])))
Bs=get(useB.ibf)
res.ibfkm=allbf(ind.betas=ind.betas,dat=datos,Bs=Bs,Bmode=Bmode,parms.init=parms.init,
nsimul=nsimul,nprocess=nprocess,log.post=log.post,use.pericchi2005.fbf=NULL,
typebf=ibfkm.ind.beta,force.L=num.max.fractions,use.MCMC=use.MCMC,
log.bf.all=log.bf.all,true.sim=true.sim,no.bergscott.prior=no.bergscott.prior)
}else{res.ibfkm=NULL}
# BIC
if(calc.bic){
res.bic=allbf(ind.betas=ind.betas,dat=datos,Bs=Bs,Bmode=Bmode,parms.init=parms.init,
nsimul=nsimul,nprocess=nprocess,log.post=log.post,use.pericchi2005.fbf=NULL,
typebf=bic.ind.beta,use.MCMC=NULL,true.sim=true.sim,no.bergscott.prior=no.bergscott.prior)
}else{res.bic=NULL}
return(list(fbf=res.fbf,ibf=res.ibf,bic=res.bic,ibfkm=res.ibfkm,cpbf=res.cpbf))
}
analyse.data1cov=function(datos,calc.cpbf=TRUE,calc.bic=TRUE,calc.fbf=TRUE,calc.fbfmode=TRUE,use.pericchi2005.fbf=TRUE,
calc.ibf=TRUE,nsimul=1000,force.L=TRUE,useB="Bmode",useB.ibf="Bmode",
use.Bsuni=FALSE,use.MCMC=FALSE,model,nprocess,true.sim=NULL,use.true.sim=FALSE,onlyone=FALSE,
no.bergscott.prior=TRUE,calc.ibfkm=FALSE){
if(lmodel=="log.post.gamma") log.post=log.post.ggamma
if(lmodel=="log.post.lnormal") log.post=log.post.lnormal
if(lmodel=="log.post.weibull") log.post=log.post.weibull
if(lmodel=="log.post.lnormal.cp") log.post=log.post.lnormal.cp
# Model Index
numcov=ncol(datos)-2
ind.betas=index.models(numcov,onlyone=onlyone)
n=nrow(datos)
if(use.true.sim){
beta.trues=llply(ind.betas,function(x){
rr=rep(0,numcov)
rr[x]=1
return(rr)})}
# Parms init
data0=as.matrix(cbind(datos[1:2],1))
if(use.true.sim){tt1=c(0,0)}else{
tt1=optim(par=c(0,0),fn=log.post,control=list(fnscale=-1),Data=data0,b=c(1,1),method=optimmethod)$par
}
tmpmyf=function(ii){
data1=as.matrix(cbind(datos[1:2],model.matrix(~.,data=datos[ind.betas[[ii]]+2])))
if(use.true.sim){pars=c(tt1,unlist(beta.trues[ii])[ind.betas[[ii]]])
logbf=log.post(parms=pars,Data=data1,b=c(1,1))-log.post(parms=tt1,Data=data0,b=c(1,1))
}else{
nunc=nunc.func(data1[,2])
XtX.inv=func.XtX.inv(rel=data1[,2],XM=cbind(data1[,-(1:3)]),y=data1[,2])
pars=optim(par=c(0,rep(0,ncol(data1)-2)),fn=log.post,control=list(fnscale=-1),Data=data1,b=c(1,1),nunc=nunc,XtX.inv=XtX.inv,method=optimmethod)$par
logbf=log.bf.parziale(data0,data1,nsimul,parms.init.used=list(tt1,pars),b=c(1,1),log.post=log.post,use.MCMC=use.MCMC)
}
return(list(pars=pars,logbf=logbf))}
ltt=tmpmyf(1)
parms.init=c(list(tt1))
log.bf.all=NULL
parms.init=ltt[1]
log.bf.all=ltt[2]
log.bf.all=unlist(log.bf.all)
nummaxpar=max(unlist(llply(ind.betas,length)))+2
# Calculates of Fraction B
if(use.Bsuni){
# All same B and same L
allB=llply(ind.betas,function(x) prob.b(n=n,ncens=n-sum(datos[,2]),p=nummaxpar,use="all"))
Bmode=llply(ind.betas,function(x) prob.b(n=n,ncens=n-sum(datos[,2]),p=nummaxpar,use="mode"))
#Bmedian=llply(ind.betas,function(x) prob.b(n=n,ncens=n-sum(datos[,2]),p=numcov+2,use="median"))
}else{
# Different B and Different L
allB=llply(ind.betas,function(x) prob.b(n=n,ncens=n-sum(datos[,2]),p=length(x)+2,use="all"))
Bmode=llply(ind.betas,function(x) prob.b(n=n,ncens=n-sum(datos[,2]),p=length(x)+2,use="mode"))
#Bmedian=llply(ind.betas,function(x) prob.b(n=n,ncens=n-sum(datos[,2]),p=length(x)+2,use="median"))
}
Bs=get(useB)
#BF con Conventional Prior:
if(calc.cpbf){
res.cpbf=allbf(ind.betas=ind.betas,dat=datos,Bs=Bs,Bmode=Bmode,parms.init=parms.init,
nsimul=nsimul,nprocess=nprocess,log.post=log.post,use.pericchi2005.fbf=NULL,
typebf=NULL,use.MCMC=NULL,true.sim=true.sim,no.bergscott.prior=no.bergscott.prior,logbf=t(t(c(0,log.bf.all))))
}else{res.cpbf=NULL}
# Fractional BF
if(calc.fbf){
res.fbf=allbf(ind.betas=ind.betas,dat=datos,Bs=Bs,Bmode=Bmode,parms.init=parms.init,
nsimul=nsimul,nprocess=nprocess,log.post=log.post,use.pericchi2005.fbf=use.pericchi2005.fbf,
typebf=fbf.ind.beta,use.MCMC=use.MCMC,log.bf.all=log.bf.all,true.sim=true.sim,
use.true.sim=use.true.sim,no.bergscott.prior=no.bergscott.prior)
}else{res.fbf=NULL}
# Intrinsic BF
if(calc.ibf){
num.max.fractions=NULL
if(force.L) num.max.fractions=max(aaply(1:length(ind.betas),.margins=1,.fun=function(i) length(allB[[i]]$B[,1])))
Bs=get(useB.ibf)
res.ibf=allbf(ind.betas=ind.betas,dat=datos,Bs=Bs,Bmode=Bmode,parms.init=parms.init,
nsimul=nsimul,nprocess=nprocess,log.post=log.post,use.pericchi2005.fbf=NULL,
typebf=ibf.ind.beta,force.L=num.max.fractions,use.MCMC=use.MCMC,
log.bf.all=log.bf.all,true.sim=true.sim,no.bergscott.prior=no.bergscott.prior)
}else{res.ibf=NULL}
# KM Intrinsic BF
if(calc.ibfkm){
num.max.fractions=NULL
if(force.L) num.max.fractions=max(aaply(1:length(ind.betas),.margins=1,.fun=function(i) length(allB[[i]]$B[,1])))
Bs=get(useB.ibf)
res.ibfkm=allbf(ind.betas=ind.betas,dat=datos,Bs=Bs,Bmode=Bmode,parms.init=parms.init,
nsimul=nsimul,nprocess=nprocess,log.post=log.post,use.pericchi2005.fbf=NULL,
typebf=ibfkm.ind.beta,force.L=num.max.fractions,use.MCMC=use.MCMC,
log.bf.all=log.bf.all,true.sim=true.sim,no.bergscott.prior=no.bergscott.prior)
}else{res.ibfkm=NULL}
# BIC
if(calc.bic){
res.bic=allbf(ind.betas=ind.betas,dat=datos,Bs=Bs,Bmode=Bmode,parms.init=parms.init,
nsimul=nsimul,nprocess=nprocess,log.post=log.post,use.pericchi2005.fbf=NULL,
typebf=bic.ind.beta,use.MCMC=NULL,true.sim=true.sim,no.bergscott.prior=no.bergscott.prior)
}else{res.bic=NULL}
return(list(fbf=res.fbf,ibf=res.ibf,bic=res.bic,ibfkm=res.ibfkm,cpbf=res.cpbf))
}
### Only BIC and FBF from pericchi 2005 approximation
analyse.data.bic.fbf=function(datos,Bs,Bmode,model,nprocess,true.sim=NULL){
if(lmodel=="log.post.lnormal") log.post=log.post.lnormal
if(lmodel=="log.post.weibull") log.post=log.post.weibull
# Calculus of Maximum(S) of Log Likelihoods for each model and each possible fraction
# For the null model
data0=as.matrix(cbind(datos[1:2],1))
log0f=rep(NA,nrow(allB[[1]]$B))
for(i in 1:nrow(allB[[1]]$B)) log0f[i]=log.marg.laplace(log.post, parms.hat=c(0,0), data0, b=allB[[1]]$B[i,])
log0.N=rep(log.marg.laplace(log.post, parms.hat=c(0,0), data0, b=c(1,1)),nrow(allB[[1]]$B))
# For the rest of models
tts=mcmapply(function(ii){
data1=as.matrix(cbind(datos[1:2],model.matrix(~.,data=datos[ind.betas[[ii]]+2])))
inits=c(0,rep(0,ncol(data1)-2))
nfrac=nrow(allB[[ii+1]]$B)
logj.N=rep(log.marg.laplace(log.post,parms.hat=inits,dat=data1,b=c(1,1)),nfrac)
logjf=logj0f=rep(NA,nfrac)
for(i in 1:nfrac){
logjf[i]=log.marg.laplace(log.post,parms.hat=inits,dat=data1,b=allB[[ii+1]]$B[i,])
logj0f[i]=log.marg.laplace(log.post,parms.hat=c(0,0),dat=data0,b=allB[[ii+1]]$B[i,])
}
return(list(logjf=logjf,logj0f=logj0f,logj.N=logj.N))},
1:length(ind.betas),mc.cores=nprocess)
llsf=c(list(log0f),tts[1,])
lls0f=c(list(log0f),tts[2,])
lls.N=c(list(log0.N),tts[3,])
# Calculates of Maximum of Log Likelihoods and Model dimension
data0=as.matrix(cbind(datos[1:2],1))
log0=optim(par=c(0,0),fn=log.post,control=list(fnscale=-1),Data=data0,b=c(1,1),method=optimmethod)$value
np0=2
tts=mcmapply(function(ii){
data1=as.matrix(cbind(datos[1:2],model.matrix(~.,data=datos[ind.betas[[ii]]+2])))
inits=c(0,rep(0,ncol(data1)-2))
logj=optim(par=inits,fn=log.post,control=list(fnscale=-1),Data=data1,b=c(1,1))$value
return(list(logj=logj,npj=length(inits)))},1:length(ind.betas),mc.cores=nprocess)
lls=c(log0,unlist(tts[1,]))
npj=c(np0,unlist(tts[2,]))
#logRf=log0f;npRf=npjf[[1]] # Encompassing from BELOW
logR=log0;npR=np0 # Encompassing from BELOW
# logR=lls[length(lls)];npR=npj[length(npj)] # Encompassing from ABOVE
# BIC
# Likelihood Evidence
EL=(lls-logR)
# Difference of Dimension
dd=(npj-npR)
# Log of BIC
lbic=EL-log(nunc)*dd*0.5
nmodels=length(llsf)
lmfbf=lfbfmode=rep(NA,nmodels)
for(i in 1:length(llsf)){
# allfbf=(lls.N[[i]]-lls.N[[nmodels]])-(llsf[[i]]-llsf[[nmodels]])
allfbf=(lls.N[[i]]-lls.N[[1]])-(llsf[[i]]-lls0f[[i]])
lmfbf[i]=log(sum(exp(allfbf)*Bs[[i]]$B.prob))
# lmfbf[i]=sum(allfbf*Bs[[i]]$B.prob)
lfbfmode[i]=allfbf[Bs[[i]]$B.prob==max(Bs[[i]]$B.prob)]
}
logbf=cbind(lbic,lfbfmode,lmfbf)
colnames(logbf)=c("bic","fbfmode","mfbf")
res=allbf(ind.betas,dat=datos,Bs=NULL,Bmode=NULL,parms.init=NULL,nsimul=NULL,use.pericchi2005.fbf=NULL,
nprocess=NULL,log.post=NULL,typebf=NULL,force.L=NULL,use.MCMC=NULL,log.bf.all=NULL,
true.sim=true.sim,use.true.sim=NULL,logbf=logbf)
return(res)
}
############################ ANALYSE SIMULATIONS ######################
collect.sim=function(results,ibfkm=FALSE){
aibflab=c("aibf","kmaibf")[1+ibfkm]
mibflab=c("mibf","kmmibf")[1+ibfkm]
write.table(x=results,file="tt.csv",sep=";",quote=FALSE)
res=read.table("tt.csv",sep=";")
res=data.frame(res[,-(1:2)])
rownames(res)=NULL
nsim=nrow(res)
namcol=colnames(res)
ids=grep("ppost",namcol)
repsim=rep(1:nsim,length(ids))
ppost=unlist(res[ids])
typebf=namcol[ids]
typebf[grep("mfbf",typebf)]="mfbf"
typebf[grep("cpbf",typebf)]="cpbf"
typebf[grep("fbfmode",typebf)]="fbfmo"
typebf[grep("bic",typebf)]="bic"