Nothing
R/adaBayes.R
In yuima: The YUIMA Project Package for SDEs
##::quasi-bayes function
setGeneric("adaBayes",
function(yuima, start,prior,lower,upper, method="mcmc",iteration=NULL,mcmc,rate=1.0,
rcpp=TRUE,algorithm="randomwalk",center=NULL,sd=NULL,rho=NULL,path=FALSE
)
standardGeneric("adaBayes")
)
#
# adabayes<- setClass("adabayes",contains = "mle",
# slots = c(mcmc="list", accept_rate="list",coef = "numeirc",
# call="call",vcov="matrix",fullcoef="numeric",model="yuima.model"))
adabayes<- setClass("adabayes",
#contains = "mle",
slots = c(mcmc="list", accept_rate="list",coef = "numeric",call="call",vcov="matrix",fullcoef="numeric"))
setMethod("adaBayes",
"yuima",
function(yuima, start,prior,lower,upper, method="mcmc",iteration=NULL,mcmc,rate=1.0,rcpp=TRUE,
algorithm="randomwalk",center=NULL,sd=NULL,rho=NULL,path=FALSE
)
{
if(length(iteration)>0){mcmc=iteration}
mean=unlist(center)
joint <- FALSE
fixed <- numeric(0)
print <- FALSE
call <- match.call()
if( missing(yuima))
yuima.stop("yuima object is missing.")
## param handling
## FIXME: maybe we should choose initial values at random within lower/upper
## at present, qmle stops
if(missing(lower) || missing(upper)){
if(missing(prior)){
lower = numeric(0)
upper = numeric(0)
pdlist <- numeric(length(yuima@model@parameter@all))
names(pdlist) <- yuima@model@parameter@all
for(i in 1: length(pdlist)){
lower = append(lower,-Inf)
upper = append(upper,Inf)
}
}
# yuima.stop("lower or upper is missing.")
}
diff.par <- yuima@model@parameter@diffusion
drift.par <- yuima@model@parameter@drift
jump.par <- yuima@model@parameter@jump
measure.par <- yuima@model@parameter@measure
common.par <- yuima@model@parameter@common
## BEGIN Prior construction
if(!missing(prior)){
priorLower = numeric(0)
priorUpper = numeric(0)
pdlist <- numeric(length(yuima@model@parameter@all))
names(pdlist) <- yuima@model@parameter@all
for(i in 1: length(pdlist)){
if(prior[[names(pdlist)[i]]]$measure.type=="code"){
expr <- prior[[names(pdlist)[i]]]$df
code <- suppressWarnings(sub("^(.+?)\\(.+", "\\1", expr, perl=TRUE))
args <- unlist(strsplit(suppressWarnings(sub("^.+?\\((.+)\\)", "\\1", expr, perl=TRUE)), ","))
pdlist[i] <- switch(code,
dunif=paste("function(z){return(dunif(z, ", args[2], ", ", args[3],"))}"),
dnorm=paste("function(z){return(dnorm(z,", args[2], ", ", args[3], "))}"),
dbeta=paste("function(z){return(dbeta(z, ", args[2], ", ", args[3], "))}"),
dgamma=paste("function(z){return(dgamma(z, ", args[2], ", ", args[3], "))}"),
dexp=paste("function(z){return(dexp(z, ", args[2], "))}")
)
qf <- switch(code,
dunif=paste("function(z){return(qunif(z, ", args[2], ", ", args[3],"))}"),
dnorm=paste("function(z){return(qnorm(z,", args[2], ", ", args[3], "))}"),
dbeta=paste("function(z){return(qbeta(z, ", args[2], ", ", args[3], "))}"),
dgamma=paste("function(z){return(qgamma(z, ", args[2], ", ", args[3], "))}"),
dexp=paste("function(z){return(qexp(z, ", args[2], "))}")
)
priorLower = append(priorLower,eval(parse("text"=qf))(0.00))
priorUpper = append(priorUpper,eval(parse("text"=qf))(1.00))
}
}
#20200518kaino
names(priorLower)<-names(pdlist)
names(priorUpper)<-names(pdlist)
if(missing(lower) || missing(upper)){
lower <- priorLower
upper <- priorUpper
}
else{
#20200518kaino
#if(sum(unlist(lower)<priorLower) + sum(unlist(upper)>priorUpper) > 0){
if(sum(unlist(lower)<priorLower[names(lower)]) + sum(unlist(upper)>priorUpper[names(upper)]) > 0){
yuima.stop("lower&upper of prior are out of parameter space.")
}
}
#20200518
#names(lower) <- names(pdlist)
#names(upper) <- names(pdlist)
pd <- function(param){
value <- 1
for(i in 1:length(pdlist)){
value <- value*eval(parse(text=pdlist[[i]]))(param[[i]])
}
return(value)
}
}else{
pd <- function(param) return(1)
}
## END Prior construction
if(!is.list(lower)){
lower <- as.list(lower)
}
if(!is.list(upper)){
upper <- as.list(upper)
}
JointOptim <- joint
if(length(common.par)>0){
JointOptim <- TRUE
yuima.warn("Drift and diffusion parameters must be different. Doing
joint estimation, asymptotic theory may not hold true.")
}
if(length(jump.par)+length(measure.par)>0)
yuima.stop("Cannot estimate the jump models, yet")
fulcoef <- NULL
if(length(diff.par)>0)
fulcoef <- diff.par
if(length(drift.par)>0)
fulcoef <- c(fulcoef, drift.par)
if(length(yuima@model@parameter@common)>0){
fulcoef<-unique(fulcoef)
}
fixed.par <- names(fixed)
if (any(!(fixed.par %in% fulcoef)))
yuima.stop("Some named arguments in 'fixed' are not arguments to the supplied yuima model")
nm <- names(start)
npar <- length(nm)
oo <- match(nm, fulcoef)
if(any(is.na(oo)))
yuima.stop("some named arguments in 'start' are not arguments to the supplied yuima model")
start <- start[order(oo)]
if(!missing(prior)){
#20200525kaino
#pdlist <- pdlist[order(oo)]
pdlist <- pdlist[names(start)]
}
nm <- names(start)
idx.diff <- match(diff.par, nm)
idx.drift <- match(drift.par,nm)
if(length(common.par)>0){
idx.common=match(common.par,nm)
idx.drift=setdiff(idx.drift,idx.common)
}
idx.fixed <- match(fixed.par, nm)
tmplower <- as.list( rep( -Inf, length(nm)))
names(tmplower) <- nm
if(!missing(lower)){
idx <- match(names(lower), names(tmplower))
if(any(is.na(idx)))
yuima.stop("names in 'lower' do not match names fo parameters")
tmplower[ idx ] <- lower
}
lower <- tmplower
tmpupper <- as.list( rep( Inf, length(nm)))
names(tmpupper) <- nm
if(!missing(upper)){
idx <- match(names(upper), names(tmpupper))
if(any(is.na(idx)))
yuima.stop("names in 'lower' do not match names fo parameters")
tmpupper[ idx ] <- upper
}
upper <- tmpupper
d.size <- yuima@model@equation.number
#20200601kaino
if (is.CARMA(yuima)){
# 24/12
d.size <-1
}
n <- length(yuima)[1]
G <- rate
if(G<=0 || G>1){
yuima.stop("rate G should be 0 < G <= 1")
}
n_0 <- floor(n^G)
if(n_0 < 2) n_0 <- 2
#######data is reduced to n_0 before qmle(16/11/2016)
env <- new.env()
#assign("X", yuima@data@original.data[1:n_0,], envir=env)
assign("X", as.matrix(onezoo(yuima)[1:n_0,]), envir=env)
assign("deltaX", matrix(0, n_0 - 1, d.size), envir=env)
assign("time", as.numeric(index(yuima@data@zoo.data[[1]])), envir=env)
#20200601kaino
if (is.CARMA(yuima)){
#24/12 If we consider a carma model,
# the observations are only the first column of env$X
# assign("X", as.matrix(onezoo(yuima)), envir=env)
# env$X<-as.matrix(env$X[,1])
# assign("deltaX", matrix(0, n-1, d.size)[,1], envir=env)
env$X<-as.matrix(env$X[,1])
env$deltaX<-as.matrix(env$deltaX[,1])
assign("time.obs",length(env$X),envir=env)
assign("p", yuima@model@info@p, envir=env)
assign("q", yuima@model@info@q, envir=env)
assign("V_inf0", matrix(diag(rep(1,env$p)),env$p,env$p), envir=env)
}
assign("Cn.r", rep(1,n_0-1), envir=env)
for(t in 1:(n_0-1))
env$deltaX[t,] <- env$X[t+1,] - env$X[t,]
assign("h", deltat(yuima@data@zoo.data[[1]]), envir=env)
pp<-0
if(env$h >= 1){
qq <- 2/G
}else{
while(1){
if(n*env$h^pp < 0.1) break
pp <- pp + 1
}
qq <- max(pp,2/G)
}
C.temper.diff <- n_0^(2/(qq*G)-1) #this is used in pg.
C.temper.drift <- (n_0*env$h)^(2/(qq*G)-1) #this is used in pg.
mle <- qmle(yuima, "start"=start, "lower"=lower,"upper"=upper, "method"="L-BFGS-B",rcpp=rcpp)
start <- as.list(mle@coef)
sigma.diff=NULL
sigma.drift=NULL
if(length(sd)<npar){
sigma=mle@vcov;
}
# if(length(diff.par)>0){
# sigma.diff=diag(1,length(diff.par))
# for(i in 1:length(diff.par)){
# sigma.diff[i,i]=sd[[diff.par[i]]]
# }
# }
#
# if(length(drift.par)>0){
# sigma.drift=diag(1,length(drift.par))
# for(i in 1:length(drift.par)){
# sigma.drift[i,i]=sd[[drift.par[i]]]
# }
# }
#}
# mu.diff=NULL
# mu.drift=NULL
#
#
# if(length(mean)<npar){
# mu.diff=NULL
# mu.drift=NULL}
#else{
# if(length(diff.par)>0){
# for(i in 1:length(diff.par)){
# mu.diff[i]=mean[[diff.par[i]]]
# }
# }
#
# if(length(drift.par)>0){
# for(i in 1:length(drift.par)){
# mu.drift[i]=mean[[drift.par[i]]]
# }
# }
# }
####mpcn make proposal
sqn<-function(x,LL){
vv=x%*%LL
zz=sum(vv*vv)
if(zz<0.0000001){
zz=0.0000001
}
return(zz)
}
# mpro<-function(mu,sample,low,up,rho,LL,L){
# d=length(mu);
# tmp=mu+sqrt(rho)*(sample-mu);
# tmp = mu+sqrt(rho)*(sample-mu);
# dt=(sample-mu)%*%LL%*%(sample-mu)
# tmp2 = 2.0/dt;
# while(1){
# prop = tmp+rnorm(d)*L*sqrt((1.0-rho)/rgamma(1,0.5*d,tmp2));
# if((sum(low>prop)+sum(up<prop))==0) break;
# }
# return(prop)
# }
make<-function(mu,sample,low,up,rho,LL,L){
d=length(mu);
tmp=mu+sqrt(rho)*(sample-mu);
dt=sqn(sample-mu,LL);
tmp2 = 2.0/dt;
prop = tmp+rnorm(d)%*%L*sqrt((1.0-rho)/rgamma(1,0.5*d,scale=tmp2));
# for(i in 1:100){
# prop = tmp+rnorm(d)*sqrt((1.0-rho)/rgamma(1,0.5*d,tmp2));
# if((sum(low>prop)+sum(up<prop))==0){break;}
# flg=flg+1;
# }
# if(flg>100){print("error")}else{
# return(prop)
# }
return(prop)
}
integ <- function(idx.fixed=NULL,f=f,start=start,par=NULL,hessian=FALSE,upper,lower){
if(length(idx.fixed)==0){
intf <- hcubature(f,lowerLimit=unlist(lower),upperLimit=unlist(upper),fDim=(length(upper)+1))$integral
}else{
intf <- hcubature(f,lowerLimit=unlist(lower[-idx.fixed]),upperLimit=unlist(upper[-idx.fixed]),fDim=(length(upper[-idx.fixed])+1))$integral
}
return(intf[-1]/intf[1])
}
mcinteg <- function(idx.fixed=NULL,f=f,p,start=start,par=NULL,hessian=FALSE,upper,lower,mean,vcov,mcmc){
#vcov=vcov[nm,nm];#Song add
if(setequal(unlist(drift.par),unlist(diff.par))&(length(mean)>length(diff.par))){mean=mean[1:length(diff.par)]}
if(length(idx.fixed)==0){#only have drift or diffusion part
intf <- mcintegrate(f,p,lowerLimit=lower,upperLimit=upper,mean,vcov,mcmc)
}else{
intf <- mcintegrate(f,p,lowerLimit=lower[-idx.fixed],upperLimit=upper[-idx.fixed],mean[-idx.fixed],vcov[-idx.fixed,-idx.fixed],mcmc)
}
return(intf)
}
mcintegrate <- function(f,p, lowerLimit, upperLimit,mean,vcov,mcmc){
acc=0
if(algorithm=="randomwalk"){
x_c <- mean
if(path){
X <- matrix(0,mcmc,length(mean))
acc=0
}
nm=length(mean)
if(length(vcov)!=(nm^2)){
vcov=diag(1,nm,nm)*vcov[1:nm,1:nm]
}
sigma=NULL
if(length(sd)>0&length(sd)==npar){
sigma=diag(sd,npar,npar);
if(length(idx.fixed)>0){
vcov=sigma[-idx.fixed,-idx.fixed]
}else{
vcov=sigma;
}
}
p_c <- p(x_c)
val <- f(x_c)
if(path) X[1,] <- x_c
# if(length(mean)>1){
# x <- rmvnorm(mcmc-1,mean,vcov)
# q <- dmvnorm(x,mean,vcov)
# q_c <- dmvnorm(mean,mean,vcov)
# }else{
# x <- rnorm(mcmc-1,mean,sqrt(vcov))
# q <- dnorm(x,mean,sqrt(vcov))
# q_c <- dnorm(mean,mean,sqrt(vcov))
# }
#
for(i in 1:(mcmc-1)){
if(length(mean)>1){
x_n <- rmvnorm(1,x_c,vcov)
#if(sum(x_n<lowerLimit)==0 & sum(x_n>upperLimit)==0) break
}else{
x_n <- rnorm(1,x_c,sqrt(vcov))
#if(sum(x_n<lowerLimit)==0 & sum(x_n>upperLimit)==0) break
}
if(sum(x_n<lowerLimit)==0 & sum(x_n>upperLimit)==0){
p_n <- p(x_n)
u <- log(runif(1))
a <- p_n-p_c
if(u<a){
p_c <- p_n
# q_c <- q_n
x_c <- x_n
acc=acc+1
}
}
if(path) X[i+1,] <- x_c
#}
val <- val+f(x_c)
}
if(path){
return(list(val=unlist(val/mcmc),X=X,acc=acc/mcmc))
}else{
return(list(val=unlist(val/mcmc)))
}
}
else if(tolower(algorithm)=="mpcn"){ #MpCN
if(path) X <- matrix(0,mcmc,length(mean))
if((sum(idx.diff==idx.fixed)==0)){
if(length(center)>0){
if(length(idx.fixed)>0){
mean =unlist(center[-idx.fixed])
}else{
mean =unlist(center) ##drift or diffusion parameter only
}
}
}
# if((sum(idx.diff==idx.fixed)>0)){
# if(length(center)>0){
# mean =unlist(center[-idx.fixed])
# }
# }
x_n <- mean
val <- mean
L=diag(1,length(mean));LL=L;
nm=length(mean)
LL=vcov;
if(length(vcov)!=(nm^2)){
LL=diag(1,nm,nm)
}
if(length(sd)>0&length(sd)==npar){
sigma=diag(sd,npar,npar);
if(length(idx.fixed)>0){
LL=sigma[-idx.fixed,-idx.fixed]
}else{
LL=sigma;
}
}
# if(length(pre_conditionnal)==1){
# LL=t(chol(solve(vcov)));L=chol(vcov);
# }
if(length(rho)==0){rho=0.8}
logLik_old <- p(x_n)+0.5*length(mean)*log(sqn(x_n-mean,LL))
if(path) X[1,] <- x_n
for(i in 1:(mcmc-1)){
prop <- make(mean,x_n,unlist(lowerLimit),unlist(upperLimit),rho,LL,L)
if((sum(prop<lowerLimit)+sum(prop>upperLimit))==0){
logLik_new <- p(prop)+0.5*length(mean)*log(sqn(prop-mean,LL))
u <- log(runif(1))
if( logLik_new-logLik_old > u){
x_n <- prop
logLik_old <- logLik_new
acc=acc+1
}
}
if(path) X[i+1,] <- x_n
val <- val+f(x_n)
}
#return(unlist(val/mcmc))
if(path){
return(list(val=unlist(val/mcmc),X=X,acc=acc/mcmc))
}else{
return(list(val=unlist(val/mcmc)))
}
}
}
#print(mle@coef)
flagNotPosDif <- 0
for(i in 1:npar){
if(mle@vcov[i,i] <= 0) flagNotPosDif <- 1 #Check mle@vcov is positive difinite matrix
}
if(flagNotPosDif == 1){
mle@vcov <- diag(c(rep(1 / n_0,length(diff.par)),rep(1 / (n_0 * env$h), npar-length(diff.par)))) # Redifine mle@vcov
}
# cov.diff=mle@vcov[1:length(diff.par),1:length(diff.par)]
# cov.drift=mle@vcov[(length(diff.par)+1):(length(diff.par)+length(drift.par)),(length(diff.par)+1):(length(diff.par)+length(drift.par))]
# if(missing(cov){
# }else{
# cov.diff=cov[[1]]
# cov.drift=cov[[2]]
tmp <- minusquasilogl(yuima=yuima, param=mle@coef, print=print, env,rcpp=rcpp)
g <- function(p,fixed,idx.fixed){
mycoef <- mle@coef
#mycoef <- as.list(p)
if(length(idx.fixed)>0){
mycoef[-idx.fixed] <- p
mycoef[idx.fixed] <- fixed
}else{
mycoef <- as.list(p)
names(mycoef) <- nm
}
return(c(1,p)*exp(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)+tmp)*pd(param=mycoef))
}
pg <- function(p,fixed,idx.fixed){
mycoef <- start
#mycoef <- as.list(p)
if(length(idx.fixed)>0){
mycoef[-idx.fixed] <- p
mycoef[idx.fixed] <- fixed
}else{
mycoef <- as.list(p)
names(mycoef) <- nm
}
# mycoef <- as.list(p)
# names(mycoef) <- nm
#return(exp(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env)+tmp)*pd(param=mycoef))
#return(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)+tmp+log(pd(param=mycoef)))#log
if(sum(idx.diff==idx.fixed)>0){
#return(C.temper.diff*(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)+tmp+log(pd(param=mycoef))))#log
return(C.temper.drift*(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)+tmp+log(pd(param=mycoef))))#log
}else{
#return(C.temper.drift*(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)+tmp+log(pd(param=mycoef))))#log
return(C.temper.diff*(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)+tmp+log(pd(param=mycoef))))#log
}
}
idf <- function(p){return(p)}
# fj <- function(p) {
# mycoef <- as.list(p)
# names(mycoef) <- nm
# mycoef[fixed.par] <- fixed
# minusquasilogl(yuima=yuima, param=mycoef, print=print, env)
# }
X.diff <- NULL
X.drift <- NULL
oout <- NULL
HESS <- matrix(0, length(nm), length(nm))
colnames(HESS) <- nm
rownames(HESS) <- nm
HaveDriftHess <- FALSE
HaveDiffHess <- FALSE
if(length(start)){
# if(JointOptim){ ### joint optimization
# if(length(start)>1){ #multidimensional optim
# oout <- optim(start, fj, method = method, hessian = TRUE, lower=lower, upper=upper)
# HESS <- oout$hessian
# HaveDriftHess <- TRUE
# HaveDiffHess <- TRUE
# } else { ### one dimensional optim
# opt1 <- optimize(f, ...) ## an interval should be provided
# opt1 <- list(par=integ(f=f,upper=upper,lower=lower,fDim=length(lower)+1),objective=0)
# oout <- list(par = opt1$minimum, value = opt1$objective)
# } ### endif( length(start)>1 )
# } else { ### first diffusion, then drift
theta1 <- NULL
acc.diff<-NULL
old.fixed <- fixed
old.start <- start
if(length(idx.diff)>0){
## DIFFUSION ESTIMATIOn first
old.fixed <- fixed
old.start <- start
new.start <- start[idx.diff] # considering only initial guess for diffusion
new.fixed <- fixed
if(length(idx.drift)>0)
new.fixed[nm[idx.drift]] <- start[idx.drift]
fixed <- new.fixed
fixed.par <- names(fixed)
idx.fixed <- match(fixed.par, nm)
names(new.start) <- nm[idx.diff]
f <- function(p){return(g(p,fixed,idx.fixed))}
pf <- function(p){return(pg(p,fixed,idx.fixed))}
if(length(unlist(new.start))>1){
# oout <- do.call(optim, args=mydots)
if(method=="mcmc"){
#oout <- list(par=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc))
mci=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc)
X.diff=mci$X
acc.diff=mci$acc
oout <- list(par=mci$val)
}else{
oout <- list(par=integ(idx.fixed=idx.fixed,f=f,upper=upper,lower=lower,start=start))
}
} else {
# opt1 <- do.call(optimize, args=mydots)
if(method=="mcmc"){
#opt1 <- list(minimum=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc))
mci=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc)
if(path) X.diff=mci$X
acc.diff=mci$acc
opt1 <- list(minimum=mci$val)
}else{
opt1 <- list(minimum=integ(idx.fixed=idx.fixed,f=f,upper=upper,lower=lower))
}
theta1 <- opt1$minimum
#names(theta1) <- diff.par
# oout <- list(par = theta1, value = opt1$objective)
oout <- list(par=theta1,value=0)
}
theta1 <- oout$par
#names(theta1) <- nm[idx.diff]
names(theta1) <- diff.par
} ## endif(length(idx.diff)>0)
theta2 <- NULL
acc.drift<-NULL
if(length(idx.drift)>0 & setequal(unlist(drift.par),unlist(diff.par))==FALSE){
## DRIFT estimation with first state diffusion estimates
fixed <- old.fixed
start <- old.start
new.start <- start[idx.drift] # considering only initial guess for drift
new.fixed <- fixed
new.fixed[names(theta1)] <- theta1
fixed <- new.fixed
fixed.par <- names(fixed)
idx.fixed <- match(fixed.par, nm)
names(new.start) <- nm[idx.drift]
f <- function(p){return(g(p,fixed,idx.fixed))}
pf <- function(p){return(pg(p,fixed,idx.fixed))}
if(length(unlist(new.start))>1){
# oout1 <- do.call(optim, args=mydots)
if(method=="mcmc"){
#oout1 <- list(par=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc))
mci=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc)
if(path) X.drift=mci$X
acc.drift=mci$acc
#20200601kaino
#oout1 <- list(minimum=mci$val)
oout1 <- list(par=mci$val)
}else{
oout1 <- list(par=integ(idx.fixed=idx.fixed,f=f,upper=upper,lower=lower))
}
} else {
# opt1 <- do.call(optimize, args=mydots)
if(method=="mcmc"){
#opt1 <- list(minimum=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc))
mci=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc)
X.drift=mci$X
acc.drift=mci$acc
opt1 <- list(minimum=mci$val)
}else{
opt1 <- list(minimum=integ(idx.fixed=idx.fixed,f=f,upper=upper,lower=lower))
}
theta2 <- opt1$minimum
names(theta2) <-nm[-idx.fixed]
oout1 <- list(par = theta2, value = as.numeric(opt1$objective))
}
theta2 <- oout1$par
} ## endif(length(idx.drift)>0)
oout1 <- list(par=c(theta1, theta2))
names(oout1$par) <-nm # c(diff.par,drift.par)
oout <- oout1
# } ### endif JointOptim
} else {
list(par = numeric(0L), value = f(start))
}
# if(path){
# return(list(coef=oout$par,X.diff=X.diff,X.drift=X.drift,accept_rate=accept_rate))
# }else{
# return(list(coef=oout$par))
# }
fDrift <- function(p) {
mycoef <- as.list(p)
names(mycoef) <- drift.par
mycoef[diff.par] <- coef[diff.par]
minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)
}
fDiff <- function(p) {
mycoef <- as.list(p)
names(mycoef) <- diff.par
mycoef[drift.par] <- coef[drift.par]
minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)
}
coef <- oout$par
control=list()
par <- coef
names(par) <- nm
#names(par) <- c(diff.par, drift.par)
#nm <- c(diff.par, drift.par)
# print(par)
# print(coef)
conDrift <- list(trace = 5, fnscale = 1,
parscale = rep.int(5, length(drift.par)),
ndeps = rep.int(0.001, length(drift.par)), maxit = 100L,
abstol = -Inf, reltol = sqrt(.Machine$double.eps), alpha = 1,
beta = 0.5, gamma = 2, REPORT = 10, type = 1, lmm = 5,
factr = 1e+07, pgtol = 0, tmax = 10, temp = 10)
conDiff <- list(trace = 5, fnscale = 1,
parscale = rep.int(5, length(diff.par)),
ndeps = rep.int(0.001, length(diff.par)), maxit = 100L,
abstol = -Inf, reltol = sqrt(.Machine$double.eps), alpha = 1,
beta = 0.5, gamma = 2, REPORT = 10, type = 1, lmm = 5,
factr = 1e+07, pgtol = 0, tmax = 10, temp = 10)
# nmsC <- names(con)
# if (method == "Nelder-Mead")
# con$maxit <- 500
# if (method == "SANN") {
# con$maxit <- 10000
# con$REPORT <- 100
# }
# con[(namc <- names(control))] <- control
# if (length(noNms <- namc[!namc %in% nmsC]))
# warning("unknown names in control: ", paste(noNms, collapse = ", "))
# if (con$trace < 0)
# warning("read the documentation for 'trace' more carefully")
# else if (method == "SANN" && con$trace && as.integer(con$REPORT) ==
# 0)
# stop("'trace != 0' needs 'REPORT >= 1'")
# if (method == "L-BFGS-B" && any(!is.na(match(c("reltol",
# "abstol"), namc))))
# warning("method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and 'abstol'")
# npar <- length(par)
# if (npar == 1 && method == "Nelder-Mead")
# warning("one-diml optimization by Nelder-Mead is unreliable: use optimize")
#
if(!HaveDriftHess & (length(drift.par)>0)){
#hess2 <- .Internal(optimhess(coef[drift.par], fDrift, NULL, conDrift))
hess2 <- optimHess(coef[drift.par], fDrift, NULL, control=conDrift)
HESS[drift.par,drift.par] <- hess2
}
if(!HaveDiffHess & (length(diff.par)>0)){
#hess1 <- .Internal(optimhess(coef[diff.par], fDiff, NULL, conDiff))
hess1 <- optimHess(coef[diff.par], fDiff, NULL, control=conDiff)
HESS[diff.par,diff.par] <- hess1
}
oout$hessian <- HESS
# vcov <- if (length(coef))
# solve(oout$hessian)
# else matrix(numeric(0L), 0L, 0L)
mycoef <- as.list(coef)
names(mycoef) <- nm
mycoef[fixed.par] <- fixed
#min <- minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)
#
# new("mle", call = call, coef = coef, fullcoef = unlist(mycoef),
#
# # vcov = vcov, min = min, details = oout, minuslogl = minusquasilogl,
# vcov = vcov, details = oout,
# method = method)
mcmc_sample<-cbind(X.diff,X.drift)
mcmc<-list()
lf=length(diff.par)
vcov=matrix(0,npar,npar)
if(path){
if(lf>1){
vcov[1:lf,1:lf]=cov(X.diff)
}else if(lf==1){
vcov[1:lf,1:lf]=var(X.diff)
}
if((npar-lf)>1){
vcov[(lf+1):npar,(lf+1):npar]=cov(X.drift)
}else if((npar-lf)==1){
vcov[(lf+1):npar,(lf+1):npar]=var(X.drift)
}
}
accept_rate<-list()
if(path){
for(i in 1:npar){
mcmc[[i]]=as.mcmc(mcmc_sample[,i])
}
#para_drift=yuima@model@parameter@drift[yuima@model@parameter@drift!=yuima@model@parameter@common]
names(mcmc) <-nm;# c(yuima@model@parameter@diffusion,para_drift)
accept_rate=list(acc.drift,acc.diff)
names(accept_rate)<-c("accepte.rate.drift","accepte.rate.diffusion")
}else{
mcmc=list("NULL")
accept_rate=list("NULL")
}
fulcoef = unlist(mycoef);
new("adabayes",mcmc=mcmc, accept_rate=accept_rate,coef=fulcoef,call=call,vcov=vcov,fullcoef=fulcoef)
}
)
setGeneric("coef")
setMethod("coef", "adabayes", function(object) object@fullcoef )
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##::quasi-bayes function
setGeneric("adaBayes",
function(yuima, start,prior,lower,upper, method="mcmc",iteration=NULL,mcmc,rate=1.0,
rcpp=TRUE,algorithm="randomwalk",center=NULL,sd=NULL,rho=NULL,path=FALSE
)
standardGeneric("adaBayes")
)
#
# adabayes<- setClass("adabayes",contains = "mle",
# slots = c(mcmc="list", accept_rate="list",coef = "numeirc",
# call="call",vcov="matrix",fullcoef="numeric",model="yuima.model"))
adabayes<- setClass("adabayes",
#contains = "mle",
slots = c(mcmc="list", accept_rate="list",coef = "numeric",call="call",vcov="matrix",fullcoef="numeric"))
setMethod("adaBayes",
"yuima",
function(yuima, start,prior,lower,upper, method="mcmc",iteration=NULL,mcmc,rate=1.0,rcpp=TRUE,
algorithm="randomwalk",center=NULL,sd=NULL,rho=NULL,path=FALSE
)
{
if(length(iteration)>0){mcmc=iteration}
mean=unlist(center)
joint <- FALSE
fixed <- numeric(0)
print <- FALSE
call <- match.call()
if( missing(yuima))
yuima.stop("yuima object is missing.")
## param handling
## FIXME: maybe we should choose initial values at random within lower/upper
## at present, qmle stops
if(missing(lower) || missing(upper)){
if(missing(prior)){
lower = numeric(0)
upper = numeric(0)
pdlist <- numeric(length(yuima@model@parameter@all))
names(pdlist) <- yuima@model@parameter@all
for(i in 1: length(pdlist)){
lower = append(lower,-Inf)
upper = append(upper,Inf)
}
}
# yuima.stop("lower or upper is missing.")
}
diff.par <- yuima@model@parameter@diffusion
drift.par <- yuima@model@parameter@drift
jump.par <- yuima@model@parameter@jump
measure.par <- yuima@model@parameter@measure
common.par <- yuima@model@parameter@common
## BEGIN Prior construction
if(!missing(prior)){
priorLower = numeric(0)
priorUpper = numeric(0)
pdlist <- numeric(length(yuima@model@parameter@all))
names(pdlist) <- yuima@model@parameter@all
for(i in 1: length(pdlist)){
if(prior[[names(pdlist)[i]]]$measure.type=="code"){
expr <- prior[[names(pdlist)[i]]]$df
code <- suppressWarnings(sub("^(.+?)\\(.+", "\\1", expr, perl=TRUE))
args <- unlist(strsplit(suppressWarnings(sub("^.+?\\((.+)\\)", "\\1", expr, perl=TRUE)), ","))
pdlist[i] <- switch(code,
dunif=paste("function(z){return(dunif(z, ", args[2], ", ", args[3],"))}"),
dnorm=paste("function(z){return(dnorm(z,", args[2], ", ", args[3], "))}"),
dbeta=paste("function(z){return(dbeta(z, ", args[2], ", ", args[3], "))}"),
dgamma=paste("function(z){return(dgamma(z, ", args[2], ", ", args[3], "))}"),
dexp=paste("function(z){return(dexp(z, ", args[2], "))}")
)
qf <- switch(code,
dunif=paste("function(z){return(qunif(z, ", args[2], ", ", args[3],"))}"),
dnorm=paste("function(z){return(qnorm(z,", args[2], ", ", args[3], "))}"),
dbeta=paste("function(z){return(qbeta(z, ", args[2], ", ", args[3], "))}"),
dgamma=paste("function(z){return(qgamma(z, ", args[2], ", ", args[3], "))}"),
dexp=paste("function(z){return(qexp(z, ", args[2], "))}")
)
priorLower = append(priorLower,eval(parse("text"=qf))(0.00))
priorUpper = append(priorUpper,eval(parse("text"=qf))(1.00))
}
}
#20200518kaino
names(priorLower)<-names(pdlist)
names(priorUpper)<-names(pdlist)
if(missing(lower) || missing(upper)){
lower <- priorLower
upper <- priorUpper
}
else{
#20200518kaino
#if(sum(unlist(lower)<priorLower) + sum(unlist(upper)>priorUpper) > 0){
if(sum(unlist(lower)<priorLower[names(lower)]) + sum(unlist(upper)>priorUpper[names(upper)]) > 0){
yuima.stop("lower&upper of prior are out of parameter space.")
}
}
#20200518
#names(lower) <- names(pdlist)
#names(upper) <- names(pdlist)
pd <- function(param){
value <- 1
for(i in 1:length(pdlist)){
value <- value*eval(parse(text=pdlist[[i]]))(param[[i]])
}
return(value)
}
}else{
pd <- function(param) return(1)
}
## END Prior construction
if(!is.list(lower)){
lower <- as.list(lower)
}
if(!is.list(upper)){
upper <- as.list(upper)
}
JointOptim <- joint
if(length(common.par)>0){
JointOptim <- TRUE
yuima.warn("Drift and diffusion parameters must be different. Doing
joint estimation, asymptotic theory may not hold true.")
}
if(length(jump.par)+length(measure.par)>0)
yuima.stop("Cannot estimate the jump models, yet")
fulcoef <- NULL
if(length(diff.par)>0)
fulcoef <- diff.par
if(length(drift.par)>0)
fulcoef <- c(fulcoef, drift.par)
if(length(yuima@model@parameter@common)>0){
fulcoef<-unique(fulcoef)
}
fixed.par <- names(fixed)
if (any(!(fixed.par %in% fulcoef)))
yuima.stop("Some named arguments in 'fixed' are not arguments to the supplied yuima model")
nm <- names(start)
npar <- length(nm)
oo <- match(nm, fulcoef)
if(any(is.na(oo)))
yuima.stop("some named arguments in 'start' are not arguments to the supplied yuima model")
start <- start[order(oo)]
if(!missing(prior)){
#20200525kaino
#pdlist <- pdlist[order(oo)]
pdlist <- pdlist[names(start)]
}
nm <- names(start)
idx.diff <- match(diff.par, nm)
idx.drift <- match(drift.par,nm)
if(length(common.par)>0){
idx.common=match(common.par,nm)
idx.drift=setdiff(idx.drift,idx.common)
}
idx.fixed <- match(fixed.par, nm)
tmplower <- as.list( rep( -Inf, length(nm)))
names(tmplower) <- nm
if(!missing(lower)){
idx <- match(names(lower), names(tmplower))
if(any(is.na(idx)))
yuima.stop("names in 'lower' do not match names fo parameters")
tmplower[ idx ] <- lower
}
lower <- tmplower
tmpupper <- as.list( rep( Inf, length(nm)))
names(tmpupper) <- nm
if(!missing(upper)){
idx <- match(names(upper), names(tmpupper))
if(any(is.na(idx)))
yuima.stop("names in 'lower' do not match names fo parameters")
tmpupper[ idx ] <- upper
}
upper <- tmpupper
d.size <- yuima@model@equation.number
#20200601kaino
if (is.CARMA(yuima)){
# 24/12
d.size <-1
}
n <- length(yuima)[1]
G <- rate
if(G<=0 || G>1){
yuima.stop("rate G should be 0 < G <= 1")
}
n_0 <- floor(n^G)
if(n_0 < 2) n_0 <- 2
#######data is reduced to n_0 before qmle(16/11/2016)
env <- new.env()
#assign("X", yuima@data@original.data[1:n_0,], envir=env)
assign("X", as.matrix(onezoo(yuima)[1:n_0,]), envir=env)
assign("deltaX", matrix(0, n_0 - 1, d.size), envir=env)
assign("time", as.numeric(index(yuima@data@zoo.data[[1]])), envir=env)
#20200601kaino
if (is.CARMA(yuima)){
#24/12 If we consider a carma model,
# the observations are only the first column of env$X
# assign("X", as.matrix(onezoo(yuima)), envir=env)
# env$X<-as.matrix(env$X[,1])
# assign("deltaX", matrix(0, n-1, d.size)[,1], envir=env)
env$X<-as.matrix(env$X[,1])
env$deltaX<-as.matrix(env$deltaX[,1])
assign("time.obs",length(env$X),envir=env)
assign("p", yuima@model@info@p, envir=env)
assign("q", yuima@model@info@q, envir=env)
assign("V_inf0", matrix(diag(rep(1,env$p)),env$p,env$p), envir=env)
}
assign("Cn.r", rep(1,n_0-1), envir=env)
for(t in 1:(n_0-1))
env$deltaX[t,] <- env$X[t+1,] - env$X[t,]
assign("h", deltat(yuima@data@zoo.data[[1]]), envir=env)
pp<-0
if(env$h >= 1){
qq <- 2/G
}else{
while(1){
if(n*env$h^pp < 0.1) break
pp <- pp + 1
}
qq <- max(pp,2/G)
}
C.temper.diff <- n_0^(2/(qq*G)-1) #this is used in pg.
C.temper.drift <- (n_0*env$h)^(2/(qq*G)-1) #this is used in pg.
mle <- qmle(yuima, "start"=start, "lower"=lower,"upper"=upper, "method"="L-BFGS-B",rcpp=rcpp)
start <- as.list(mle@coef)
sigma.diff=NULL
sigma.drift=NULL
if(length(sd)<npar){
sigma=mle@vcov;
}
# if(length(diff.par)>0){
# sigma.diff=diag(1,length(diff.par))
# for(i in 1:length(diff.par)){
# sigma.diff[i,i]=sd[[diff.par[i]]]
# }
# }
#
# if(length(drift.par)>0){
# sigma.drift=diag(1,length(drift.par))
# for(i in 1:length(drift.par)){
# sigma.drift[i,i]=sd[[drift.par[i]]]
# }
# }
#}
# mu.diff=NULL
# mu.drift=NULL
#
#
# if(length(mean)<npar){
# mu.diff=NULL
# mu.drift=NULL}
#else{
# if(length(diff.par)>0){
# for(i in 1:length(diff.par)){
# mu.diff[i]=mean[[diff.par[i]]]
# }
# }
#
# if(length(drift.par)>0){
# for(i in 1:length(drift.par)){
# mu.drift[i]=mean[[drift.par[i]]]
# }
# }
# }
####mpcn make proposal
sqn<-function(x,LL){
vv=x%*%LL
zz=sum(vv*vv)
if(zz<0.0000001){
zz=0.0000001
}
return(zz)
}
# mpro<-function(mu,sample,low,up,rho,LL,L){
# d=length(mu);
# tmp=mu+sqrt(rho)*(sample-mu);
# tmp = mu+sqrt(rho)*(sample-mu);
# dt=(sample-mu)%*%LL%*%(sample-mu)
# tmp2 = 2.0/dt;
# while(1){
# prop = tmp+rnorm(d)*L*sqrt((1.0-rho)/rgamma(1,0.5*d,tmp2));
# if((sum(low>prop)+sum(up<prop))==0) break;
# }
# return(prop)
# }
make<-function(mu,sample,low,up,rho,LL,L){
d=length(mu);
tmp=mu+sqrt(rho)*(sample-mu);
dt=sqn(sample-mu,LL);
tmp2 = 2.0/dt;
prop = tmp+rnorm(d)%*%L*sqrt((1.0-rho)/rgamma(1,0.5*d,scale=tmp2));
# for(i in 1:100){
# prop = tmp+rnorm(d)*sqrt((1.0-rho)/rgamma(1,0.5*d,tmp2));
# if((sum(low>prop)+sum(up<prop))==0){break;}
# flg=flg+1;
# }
# if(flg>100){print("error")}else{
# return(prop)
# }
return(prop)
}
integ <- function(idx.fixed=NULL,f=f,start=start,par=NULL,hessian=FALSE,upper,lower){
if(length(idx.fixed)==0){
intf <- hcubature(f,lowerLimit=unlist(lower),upperLimit=unlist(upper),fDim=(length(upper)+1))$integral
}else{
intf <- hcubature(f,lowerLimit=unlist(lower[-idx.fixed]),upperLimit=unlist(upper[-idx.fixed]),fDim=(length(upper[-idx.fixed])+1))$integral
}
return(intf[-1]/intf[1])
}
mcinteg <- function(idx.fixed=NULL,f=f,p,start=start,par=NULL,hessian=FALSE,upper,lower,mean,vcov,mcmc){
#vcov=vcov[nm,nm];#Song add
if(setequal(unlist(drift.par),unlist(diff.par))&(length(mean)>length(diff.par))){mean=mean[1:length(diff.par)]}
if(length(idx.fixed)==0){#only have drift or diffusion part
intf <- mcintegrate(f,p,lowerLimit=lower,upperLimit=upper,mean,vcov,mcmc)
}else{
intf <- mcintegrate(f,p,lowerLimit=lower[-idx.fixed],upperLimit=upper[-idx.fixed],mean[-idx.fixed],vcov[-idx.fixed,-idx.fixed],mcmc)
}
return(intf)
}
mcintegrate <- function(f,p, lowerLimit, upperLimit,mean,vcov,mcmc){
acc=0
if(algorithm=="randomwalk"){
x_c <- mean
if(path){
X <- matrix(0,mcmc,length(mean))
acc=0
}
nm=length(mean)
if(length(vcov)!=(nm^2)){
vcov=diag(1,nm,nm)*vcov[1:nm,1:nm]
}
sigma=NULL
if(length(sd)>0&length(sd)==npar){
sigma=diag(sd,npar,npar);
if(length(idx.fixed)>0){
vcov=sigma[-idx.fixed,-idx.fixed]
}else{
vcov=sigma;
}
}
p_c <- p(x_c)
val <- f(x_c)
if(path) X[1,] <- x_c
# if(length(mean)>1){
# x <- rmvnorm(mcmc-1,mean,vcov)
# q <- dmvnorm(x,mean,vcov)
# q_c <- dmvnorm(mean,mean,vcov)
# }else{
# x <- rnorm(mcmc-1,mean,sqrt(vcov))
# q <- dnorm(x,mean,sqrt(vcov))
# q_c <- dnorm(mean,mean,sqrt(vcov))
# }
#
for(i in 1:(mcmc-1)){
if(length(mean)>1){
x_n <- rmvnorm(1,x_c,vcov)
#if(sum(x_n<lowerLimit)==0 & sum(x_n>upperLimit)==0) break
}else{
x_n <- rnorm(1,x_c,sqrt(vcov))
#if(sum(x_n<lowerLimit)==0 & sum(x_n>upperLimit)==0) break
}
if(sum(x_n<lowerLimit)==0 & sum(x_n>upperLimit)==0){
p_n <- p(x_n)
u <- log(runif(1))
a <- p_n-p_c
if(u<a){
p_c <- p_n
# q_c <- q_n
x_c <- x_n
acc=acc+1
}
}
if(path) X[i+1,] <- x_c
#}
val <- val+f(x_c)
}
if(path){
return(list(val=unlist(val/mcmc),X=X,acc=acc/mcmc))
}else{
return(list(val=unlist(val/mcmc)))
}
}
else if(tolower(algorithm)=="mpcn"){ #MpCN
if(path) X <- matrix(0,mcmc,length(mean))
if((sum(idx.diff==idx.fixed)==0)){
if(length(center)>0){
if(length(idx.fixed)>0){
mean =unlist(center[-idx.fixed])
}else{
mean =unlist(center) ##drift or diffusion parameter only
}
}
}
# if((sum(idx.diff==idx.fixed)>0)){
# if(length(center)>0){
# mean =unlist(center[-idx.fixed])
# }
# }
x_n <- mean
val <- mean
L=diag(1,length(mean));LL=L;
nm=length(mean)
LL=vcov;
if(length(vcov)!=(nm^2)){
LL=diag(1,nm,nm)
}
if(length(sd)>0&length(sd)==npar){
sigma=diag(sd,npar,npar);
if(length(idx.fixed)>0){
LL=sigma[-idx.fixed,-idx.fixed]
}else{
LL=sigma;
}
}
# if(length(pre_conditionnal)==1){
# LL=t(chol(solve(vcov)));L=chol(vcov);
# }
if(length(rho)==0){rho=0.8}
logLik_old <- p(x_n)+0.5*length(mean)*log(sqn(x_n-mean,LL))
if(path) X[1,] <- x_n
for(i in 1:(mcmc-1)){
prop <- make(mean,x_n,unlist(lowerLimit),unlist(upperLimit),rho,LL,L)
if((sum(prop<lowerLimit)+sum(prop>upperLimit))==0){
logLik_new <- p(prop)+0.5*length(mean)*log(sqn(prop-mean,LL))
u <- log(runif(1))
if( logLik_new-logLik_old > u){
x_n <- prop
logLik_old <- logLik_new
acc=acc+1
}
}
if(path) X[i+1,] <- x_n
val <- val+f(x_n)
}
#return(unlist(val/mcmc))
if(path){
return(list(val=unlist(val/mcmc),X=X,acc=acc/mcmc))
}else{
return(list(val=unlist(val/mcmc)))
}
}
}
#print(mle@coef)
flagNotPosDif <- 0
for(i in 1:npar){
if(mle@vcov[i,i] <= 0) flagNotPosDif <- 1 #Check mle@vcov is positive difinite matrix
}
if(flagNotPosDif == 1){
mle@vcov <- diag(c(rep(1 / n_0,length(diff.par)),rep(1 / (n_0 * env$h), npar-length(diff.par)))) # Redifine mle@vcov
}
# cov.diff=mle@vcov[1:length(diff.par),1:length(diff.par)]
# cov.drift=mle@vcov[(length(diff.par)+1):(length(diff.par)+length(drift.par)),(length(diff.par)+1):(length(diff.par)+length(drift.par))]
# if(missing(cov){
# }else{
# cov.diff=cov[[1]]
# cov.drift=cov[[2]]
tmp <- minusquasilogl(yuima=yuima, param=mle@coef, print=print, env,rcpp=rcpp)
g <- function(p,fixed,idx.fixed){
mycoef <- mle@coef
#mycoef <- as.list(p)
if(length(idx.fixed)>0){
mycoef[-idx.fixed] <- p
mycoef[idx.fixed] <- fixed
}else{
mycoef <- as.list(p)
names(mycoef) <- nm
}
return(c(1,p)*exp(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)+tmp)*pd(param=mycoef))
}
pg <- function(p,fixed,idx.fixed){
mycoef <- start
#mycoef <- as.list(p)
if(length(idx.fixed)>0){
mycoef[-idx.fixed] <- p
mycoef[idx.fixed] <- fixed
}else{
mycoef <- as.list(p)
names(mycoef) <- nm
}
# mycoef <- as.list(p)
# names(mycoef) <- nm
#return(exp(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env)+tmp)*pd(param=mycoef))
#return(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)+tmp+log(pd(param=mycoef)))#log
if(sum(idx.diff==idx.fixed)>0){
#return(C.temper.diff*(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)+tmp+log(pd(param=mycoef))))#log
return(C.temper.drift*(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)+tmp+log(pd(param=mycoef))))#log
}else{
#return(C.temper.drift*(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)+tmp+log(pd(param=mycoef))))#log
return(C.temper.diff*(-minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)+tmp+log(pd(param=mycoef))))#log
}
}
idf <- function(p){return(p)}
# fj <- function(p) {
# mycoef <- as.list(p)
# names(mycoef) <- nm
# mycoef[fixed.par] <- fixed
# minusquasilogl(yuima=yuima, param=mycoef, print=print, env)
# }
X.diff <- NULL
X.drift <- NULL
oout <- NULL
HESS <- matrix(0, length(nm), length(nm))
colnames(HESS) <- nm
rownames(HESS) <- nm
HaveDriftHess <- FALSE
HaveDiffHess <- FALSE
if(length(start)){
# if(JointOptim){ ### joint optimization
# if(length(start)>1){ #multidimensional optim
# oout <- optim(start, fj, method = method, hessian = TRUE, lower=lower, upper=upper)
# HESS <- oout$hessian
# HaveDriftHess <- TRUE
# HaveDiffHess <- TRUE
# } else { ### one dimensional optim
# opt1 <- optimize(f, ...) ## an interval should be provided
# opt1 <- list(par=integ(f=f,upper=upper,lower=lower,fDim=length(lower)+1),objective=0)
# oout <- list(par = opt1$minimum, value = opt1$objective)
# } ### endif( length(start)>1 )
# } else { ### first diffusion, then drift
theta1 <- NULL
acc.diff<-NULL
old.fixed <- fixed
old.start <- start
if(length(idx.diff)>0){
## DIFFUSION ESTIMATIOn first
old.fixed <- fixed
old.start <- start
new.start <- start[idx.diff] # considering only initial guess for diffusion
new.fixed <- fixed
if(length(idx.drift)>0)
new.fixed[nm[idx.drift]] <- start[idx.drift]
fixed <- new.fixed
fixed.par <- names(fixed)
idx.fixed <- match(fixed.par, nm)
names(new.start) <- nm[idx.diff]
f <- function(p){return(g(p,fixed,idx.fixed))}
pf <- function(p){return(pg(p,fixed,idx.fixed))}
if(length(unlist(new.start))>1){
# oout <- do.call(optim, args=mydots)
if(method=="mcmc"){
#oout <- list(par=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc))
mci=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc)
X.diff=mci$X
acc.diff=mci$acc
oout <- list(par=mci$val)
}else{
oout <- list(par=integ(idx.fixed=idx.fixed,f=f,upper=upper,lower=lower,start=start))
}
} else {
# opt1 <- do.call(optimize, args=mydots)
if(method=="mcmc"){
#opt1 <- list(minimum=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc))
mci=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc)
if(path) X.diff=mci$X
acc.diff=mci$acc
opt1 <- list(minimum=mci$val)
}else{
opt1 <- list(minimum=integ(idx.fixed=idx.fixed,f=f,upper=upper,lower=lower))
}
theta1 <- opt1$minimum
#names(theta1) <- diff.par
# oout <- list(par = theta1, value = opt1$objective)
oout <- list(par=theta1,value=0)
}
theta1 <- oout$par
#names(theta1) <- nm[idx.diff]
names(theta1) <- diff.par
} ## endif(length(idx.diff)>0)
theta2 <- NULL
acc.drift<-NULL
if(length(idx.drift)>0 & setequal(unlist(drift.par),unlist(diff.par))==FALSE){
## DRIFT estimation with first state diffusion estimates
fixed <- old.fixed
start <- old.start
new.start <- start[idx.drift] # considering only initial guess for drift
new.fixed <- fixed
new.fixed[names(theta1)] <- theta1
fixed <- new.fixed
fixed.par <- names(fixed)
idx.fixed <- match(fixed.par, nm)
names(new.start) <- nm[idx.drift]
f <- function(p){return(g(p,fixed,idx.fixed))}
pf <- function(p){return(pg(p,fixed,idx.fixed))}
if(length(unlist(new.start))>1){
# oout1 <- do.call(optim, args=mydots)
if(method=="mcmc"){
#oout1 <- list(par=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc))
mci=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc)
if(path) X.drift=mci$X
acc.drift=mci$acc
#20200601kaino
#oout1 <- list(minimum=mci$val)
oout1 <- list(par=mci$val)
}else{
oout1 <- list(par=integ(idx.fixed=idx.fixed,f=f,upper=upper,lower=lower))
}
} else {
# opt1 <- do.call(optimize, args=mydots)
if(method=="mcmc"){
#opt1 <- list(minimum=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc))
mci=mcinteg(idx.fixed=idx.fixed,f=idf,p=pf,upper=upper,lower=lower,mean=mle@coef,vcov=mle@vcov,mcmc=mcmc)
X.drift=mci$X
acc.drift=mci$acc
opt1 <- list(minimum=mci$val)
}else{
opt1 <- list(minimum=integ(idx.fixed=idx.fixed,f=f,upper=upper,lower=lower))
}
theta2 <- opt1$minimum
names(theta2) <-nm[-idx.fixed]
oout1 <- list(par = theta2, value = as.numeric(opt1$objective))
}
theta2 <- oout1$par
} ## endif(length(idx.drift)>0)
oout1 <- list(par=c(theta1, theta2))
names(oout1$par) <-nm # c(diff.par,drift.par)
oout <- oout1
# } ### endif JointOptim
} else {
list(par = numeric(0L), value = f(start))
}
# if(path){
# return(list(coef=oout$par,X.diff=X.diff,X.drift=X.drift,accept_rate=accept_rate))
# }else{
# return(list(coef=oout$par))
# }
fDrift <- function(p) {
mycoef <- as.list(p)
names(mycoef) <- drift.par
mycoef[diff.par] <- coef[diff.par]
minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)
}
fDiff <- function(p) {
mycoef <- as.list(p)
names(mycoef) <- diff.par
mycoef[drift.par] <- coef[drift.par]
minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)
}
coef <- oout$par
control=list()
par <- coef
names(par) <- nm
#names(par) <- c(diff.par, drift.par)
#nm <- c(diff.par, drift.par)
# print(par)
# print(coef)
conDrift <- list(trace = 5, fnscale = 1,
parscale = rep.int(5, length(drift.par)),
ndeps = rep.int(0.001, length(drift.par)), maxit = 100L,
abstol = -Inf, reltol = sqrt(.Machine$double.eps), alpha = 1,
beta = 0.5, gamma = 2, REPORT = 10, type = 1, lmm = 5,
factr = 1e+07, pgtol = 0, tmax = 10, temp = 10)
conDiff <- list(trace = 5, fnscale = 1,
parscale = rep.int(5, length(diff.par)),
ndeps = rep.int(0.001, length(diff.par)), maxit = 100L,
abstol = -Inf, reltol = sqrt(.Machine$double.eps), alpha = 1,
beta = 0.5, gamma = 2, REPORT = 10, type = 1, lmm = 5,
factr = 1e+07, pgtol = 0, tmax = 10, temp = 10)
# nmsC <- names(con)
# if (method == "Nelder-Mead")
# con$maxit <- 500
# if (method == "SANN") {
# con$maxit <- 10000
# con$REPORT <- 100
# }
# con[(namc <- names(control))] <- control
# if (length(noNms <- namc[!namc %in% nmsC]))
# warning("unknown names in control: ", paste(noNms, collapse = ", "))
# if (con$trace < 0)
# warning("read the documentation for 'trace' more carefully")
# else if (method == "SANN" && con$trace && as.integer(con$REPORT) ==
# 0)
# stop("'trace != 0' needs 'REPORT >= 1'")
# if (method == "L-BFGS-B" && any(!is.na(match(c("reltol",
# "abstol"), namc))))
# warning("method L-BFGS-B uses 'factr' (and 'pgtol') instead of 'reltol' and 'abstol'")
# npar <- length(par)
# if (npar == 1 && method == "Nelder-Mead")
# warning("one-diml optimization by Nelder-Mead is unreliable: use optimize")
#
if(!HaveDriftHess & (length(drift.par)>0)){
#hess2 <- .Internal(optimhess(coef[drift.par], fDrift, NULL, conDrift))
hess2 <- optimHess(coef[drift.par], fDrift, NULL, control=conDrift)
HESS[drift.par,drift.par] <- hess2
}
if(!HaveDiffHess & (length(diff.par)>0)){
#hess1 <- .Internal(optimhess(coef[diff.par], fDiff, NULL, conDiff))
hess1 <- optimHess(coef[diff.par], fDiff, NULL, control=conDiff)
HESS[diff.par,diff.par] <- hess1
}
oout$hessian <- HESS
# vcov <- if (length(coef))
# solve(oout$hessian)
# else matrix(numeric(0L), 0L, 0L)
mycoef <- as.list(coef)
names(mycoef) <- nm
mycoef[fixed.par] <- fixed
#min <- minusquasilogl(yuima=yuima, param=mycoef, print=print, env,rcpp=rcpp)
#
# new("mle", call = call, coef = coef, fullcoef = unlist(mycoef),
#
# # vcov = vcov, min = min, details = oout, minuslogl = minusquasilogl,
# vcov = vcov, details = oout,
# method = method)
mcmc_sample<-cbind(X.diff,X.drift)
mcmc<-list()
lf=length(diff.par)
vcov=matrix(0,npar,npar)
if(path){
if(lf>1){
vcov[1:lf,1:lf]=cov(X.diff)
}else if(lf==1){
vcov[1:lf,1:lf]=var(X.diff)
}
if((npar-lf)>1){
vcov[(lf+1):npar,(lf+1):npar]=cov(X.drift)
}else if((npar-lf)==1){
vcov[(lf+1):npar,(lf+1):npar]=var(X.drift)
}
}
accept_rate<-list()
if(path){
for(i in 1:npar){
mcmc[[i]]=as.mcmc(mcmc_sample[,i])
}
#para_drift=yuima@model@parameter@drift[yuima@model@parameter@drift!=yuima@model@parameter@common]
names(mcmc) <-nm;# c(yuima@model@parameter@diffusion,para_drift)
accept_rate=list(acc.drift,acc.diff)
names(accept_rate)<-c("accepte.rate.drift","accepte.rate.diffusion")
}else{
mcmc=list("NULL")
accept_rate=list("NULL")
}
fulcoef = unlist(mycoef);
new("adabayes",mcmc=mcmc, accept_rate=accept_rate,coef=fulcoef,call=call,vcov=vcov,fullcoef=fulcoef)
}
)
setGeneric("coef")
setMethod("coef", "adabayes", function(object) object@fullcoef )
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