# R/GQD.estimates.R In DiffusionRgqd: Inference and Analysis for Generalized Quadratic Diffusions

```GQD.estimates = function(x,thin = 100, burns, CI = c(0.05,0.95), corrmat = FALSE, acf.plot =TRUE, palette = 'mono')
{
if(class(x)=='GQD.mle')
{
sigma = sqrt(diag(solve(-x\$opt\$hessian)))
upper = x\$opt\$par+1.96*sigma
lower = x\$opt\$par-1.96*sigma
EstCI = data.frame(Estimate=x\$opt\$par, Lower_95=lower,Upper_95=upper)
form  = function(x,mm = 2){format(round(x, mm), nsmall = mm)}
rownames(EstCI) <- paste0('theta[',1:length(x\$opt\$par),']')
dat2 =data.frame(form(solve(-x\$opt\$hessian)/(sigma%o%sigma),2))
rownames(dat2) <- paste0('theta[',1:length(x\$opt\$par),']')
colnames(dat2) <- paste0('theta[',1:length(x\$opt\$par),']')
if(corrmat){return(list(estimates=data.frame(form(EstCI,3)),corrmat = dat2))}
return(data.frame(round(EstCI,3)))
}

if(class(x)=='GQD.mcmc')
{
if(missing(burns)){burns =min(round(dim(x\$par.matrix)[1]/2),25000)}
windw = seq(burns,dim(x\$par.matrix)[1],thin)
est = apply(x\$par.matrix[windw,], 2, mean)
CI=t(apply(x\$par.matrix[windw,], 2, quantile,probs = CI))
form = function(x,mm = 2){format(round(x, mm), nsmall = mm)}
dat=data.frame(cbind(form(cbind(est,CI),3)))
dat = matrix(as.numeric(as.matrix(dat)),dim(dat)[1])
rownames(dat)=paste0('theta[',1:dim(x\$par.matrix)[2],']')
colnames(dat) = c('Estimate','Lower_CI','Upper_CI')

dat2=data.frame(form(cor(x\$par.matrix[windw,])))
dat2 = matrix(as.numeric(as.matrix(dat2)),dim(dat2)[1])
rownames(dat2)=paste0('theta[',1:dim(x\$par.matrix)[2],']')
colnames(dat2)=paste0('theta[',1:dim(x\$par.matrix)[2],']')
if(acf.plot)
{
nper=dim(x\$par.matrix)[2]
if(nper==1){par(mfrow=c(1,2))}
if(nper==2){par(mfrow=c(2,2))}
if(nper==3){par(mfrow=c(2,2))}
if(nper>3)
{
d1=1:((nper)+1)
d2=d1
O=outer(d1,d2)
test=O-((nper)+1)
test[test<0]=100
test=test[1:4,1:4]
test
wh=which(test==min(test))

d1=d1[col(test)[wh[1]]]
d2=d2[row(test)[wh[1]]]
par(mfrow=c(d1,d2))
}
if(palette=='mono')
{
cols =rep('#222299',nper)
}else{
cols=rainbow_hcl(nper, start = 10, end = 275,c=100,l=70)
}
for(i in 1:dim(x\$par.matrix)[2])
{
acf(x\$par.matrix[windw,i],main=paste0('ACF: theta[',i,']\nThin=',thin,', Burns=',burns,', N=',length(windw)),col = cols[i],lwd=2)
}
}
if(corrmat){return(list(estimates = dat, corrmat = dat2))}
return(dat)
}

}
```

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DiffusionRgqd documentation built on May 2, 2019, 3:26 a.m.