#' Computes Whittle likelihood of model
#'
#' @param psi A vector of all the real hyper-parameters
#' @param mdl The specified sigex model, a list object
#' @param data.ts A T x N matrix ts object; any missing values
#' must be encoded with NA in that entry
#'
#' @return Returns Whittle likelihood, evaluated at Fourier frequencies
#' corresponding to model, for differenced time series,
#' with hyper-parameter psi.
#' @export
#'
sigex.whittle <- function(psi,mdl,data.ts)
{
##########################################################################
#
# sigex.whittle
# Copyright (C) 2017 Tucker McElroy
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#
############################################################################
################# Documentation #####################################
#
# Purpose: computes Whittle likelihood of model
# Background:
# param is the name for the model parameters entered into
# a list object with a more intuitive structure, whereas
# psi refers to a vector of real numbers containing all
# hyper-parameters (i.e., reals mapped bijectively to the parameter manifold)
# Format: psi has three portions, psi = [xi,zeta,beta]
# xi ~ all hyper-parameters for covariance matrices
# zeta ~ all hyper-parameters for t.s. models
# beta ~ all regression parameters
# Notes: does not yet handle missing values in data.ts!!!
# Inputs:
# psi: see background.
# mdl: the specified sigex model, a list object
# data.ts: a T x N matrix ts object; any missing values
# must be encoded with NA in that entry
# Outputs:
# returns Whittle likelihood, evaluated at Fourier frequencies
# corresponding to model, for differenced time series,
# with hyper-parameter psi.
# Requires: sigex.zetalen, sigex.zeta2par, sigex.param2gcd, sigex.delta,
# sigex.acf, sigex.spectra, sigex.psi2par
#
####################################################################
x <- t(data.ts)
N <- dim(x)[1]
T <- dim(x)[2]
param <- sigex.psi2par(psi,mdl,data.ts)
z <- x
z[is.na(z)] <- 1i
L.par <- mdl[[3]]
D.par <- mdl[[3]]
zeta.par <- vector("list",length(mdl[[3]]))
acf.mat <- matrix(0,nrow=N*T,ncol=N)
# get xi portion
ind <- 0
A.mat <- matrix(0,N,N)
A.mat[lower.tri(A.mat)] <- 1
for(i in 1:length(mdl[[3]]))
{
vrank <- mdl[[1]][[i]]
D.dim <- length(vrank)
L.dim <- sum(A.mat[,as.vector(vrank)])
L.psi <- NULL
if(L.dim > 0) L.psi <- psi[(ind+1):(ind+L.dim)]
ind <- ind+L.dim
D.psi <- psi[(ind+1):(ind+D.dim)]
ind <- ind+D.dim
L.mat <- sigex.param2gcd(L.psi,N,as.vector(vrank))
L.par[[i]] <- L.mat
D.par[[i]] <- D.psi
}
# get beta portion
beta.len <- 0
for(i in 1:N)
{
beta.len <- beta.len + dim(mdl[[4]][[i]])[2]
}
beta.par <- as.vector(psi[(length(psi)-beta.len+1):length(psi)])
# get zeta portion
if(length(psi)-beta.len-ind > 0) {
zeta <- psi[(ind+1):(length(psi)-beta.len)] }
ind <- 0
for(i in 1:length(mdl[[3]]))
{
mdlType <- mdl[[2]][[i]]
delta <- mdl[[3]][[i]]
zetalen <- sigex.zetalen(mdlType,N)
if(zetalen > 0) {
subzeta <- zeta[(ind+1):(ind+zetalen)]
zeta.par[[i]] <- sigex.zeta2par(subzeta,mdlType,N)
}
ind <- ind + zetalen
delta <- sigex.delta(mdl,i)
acf.mat <- acf.mat + sigex.acf(L.par[[i]],D.par[[i]],mdl,i,zeta.par[[i]],delta,T)
}
x.acf <- array(acf.mat,dim=c(N,T,N))
reg.vec <- beta.par
# subtract regression effects from available sample only
ind <- 0
for(k in 1:N)
{
reg.mat <- mdl[[4]][[k]]
len <- dim(reg.mat)[2]
z[k,] <- z[k,] - reg.mat %*% reg.vec[(ind+1):(ind+len)]
ind <- ind+len
}
# difference the data
delta <- sigex.delta(mdl,0)
x.diff <- as.matrix(stats::filter(t(z),delta,method="convolution",
sides=1)[length(delta):T,])
Tdiff <- dim(x.diff)[1]
x.diff <- t(x.diff)
if(Tdiff %% 2 == 1) { grid <- Tdiff-1 } else { grid <- Tdiff-2 }
T.m <- grid/2
f.all <- t(rep(0,grid+1) %x% diag(N))
for(i in 1:length(mdl[[3]]))
{
L.par <- param[[1]][[i]]
D.par <- param[[2]][[i]]
delta <- sigex.delta(mdl,i)
f.comp <- sigex.spectra(L.par,D.par,mdl,i,param[[3]][[i]],delta,grid)
f.all <- f.all + matrix(f.comp,nrow=N)
}
f.all <- array(f.all,c(N,N,(grid+1)))
x.dft <- NULL
for(i in 1:N)
{
x.dft <- rbind(x.dft,fft(x.diff[i,1:(grid+1)])*exp(-1i*(seq(1,grid+1)+T.m)/grid)/sqrt(grid+1))
}
whittle.lik <- 0
for(l in 1:(grid+1))
{
whittle.lik <- whittle.lik + t(Conj(x.dft[,l])) %*% solve(f.all[,,l]) %*% x.dft[,l] +
+ sum(log(Re(eigen(f.all[,,l])$values)))
}
whittle.lik <- Re(whittle.lik)/(grid+1)
print(whittle.lik)
return(whittle.lik)
}
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