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ConfidenceBands <- function(object, B = 10000L, probs = c(0.01, 0.1, 0.9, 0.99), ...) {
iB = B
vPw = object@Estimates$optimiser$pars
iT = object@ModelInfo$iT
iK = object@ModelInfo$iK
Data = getObs(object)
Dist = getDist(object)
DType = DistType(Dist)
ScalingType = getScalingType(object)
vCov = ginv(object@Estimates$optimiser$hessian)/iT
mPw = rmvnorm_mat(iB, vPw, vCov)
mTheta = getFilteredParameters(object)
cTheta = array(0, dim = c(iT + 1L, iB + 1L, iK), dimnames = list(1:(iT + 1L), 1:(iB + 1L), colnames(mTheta)))
cTheta[, 1L, ] = mTheta
for (b in 2:(iB + 1L)) {
vPw_foo = mPw[b - 1L, ]
names(vPw_foo) = names(vPw)
lParList = vPw2lPn_Uni(vPw_foo, iK)
lParList = AddFixedPar(lParList)
if (DType == "univariate")
cTheta[, b, ] = t(GASFilter_univ(Data, lParList$vKappa, lParList$mA, lParList$mB, iT, iK,
Dist, ScalingType)$mTheta)
if (DType == "multivariate")
cTheta[, b, ] = t(GASFilter_multi(Data, lParList$vKappa, lParList$mA, lParList$mB, iT, nrow(Data),
iK, Dist, ScalingType)$mTheta)
}
cQuantile = array(0, dim = c(iT + 1L, length(probs), iK), dimnames = list(1:(iT + 1L), paste("q",
probs, sep = ""), colnames(mTheta)))
for (k in 1:iK) {
cQuantile[, , k] = t(apply(cTheta[, , k], 1L, quantile, probs = probs))
}
return(cQuantile)
}
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