Nothing
### object - mixest obtained from mixest1()
### x - matrix T x m of independent variables
### m - number of independent variables
.mixest1.sim <- function(object,x)
{
y <- matrix(0,ncol=1,nrow=nrow(x))
mods <- object$components
ftype <- as.numeric(object$parameters[2])
lambda <- object$parameters[3]
if (is.na(lambda))
{
lambda <- NULL
}
else
{
lambda <- as.numeric(lambda)
}
kappa <- object$parameters[4]
if (is.na(kappa))
{
kappa <- NULL
}
else
{
kappa <- as.numeric(kappa)
}
atype <- as.numeric(object$parameters[7])
V <- as.numeric(object$parameters[5])
if (is.null(lambda) && is.null(kappa))
{
kft <- 2
}
else
{
kft <- 1
if (is.null(lambda)) { lambda <- 1 }
}
if (is.null(colnames(x)))
{
colnames(x) <- colnames(x,do.NULL=FALSE,prefix="X")
}
x <- cbind(1,x)
colnames(x)[1] <- "const"
y[1,] <- rnorm(n=1,mean=as.numeric(x[1,,drop=FALSE] %*% t(object$data.last[[1]])),sd=(object$data.last[[2]])^0.5)
T <- nrow(y)
nc <- nrow(mods)
thetas <- list()
Es <- list()
Vs <- list()
predpdfs <- matrix(0,nrow=1,ncol=nc)
y.pred <- matrix(0,ncol=nc,nrow=T+1)
w <- matrix(1/nc,nrow=T+1,ncol=nc)
w[1,] <- object$data.last[[5]]
a <- object$data.last[[6]]
v <- object$data.last[[7]]
theta.av <- matrix(0,nrow=T+1,ncol=ncol(x))
theta.av[1,] <- object$data.last[[8]]
theta.out <- theta.av
R <- list()
R[[1]] <- object$data.last[[10]]
for (i in 1:nc)
{
thetas[[i]] <- matrix(0,ncol=ncol(x),nrow=nrow(y)+1)
Es[[i]] <- diag(1,ncol(x))
Vs[[i]] <- object$data.last[[4]][[i]]
}
R.out <- matrix(1,ncol=ncol(x),nrow=T+1)
R.out[1,] <- object$data.last[[3]]
V.out <- matrix(1,ncol=1,nrow=T+1)
V.out[1,] <- object$data.last[[2]]
for (t in 1:T)
{
for (i in 1:nc)
{
x.mod <- x
x.mod[,which(mods[i,]==0)] <- 0
if (kft==1)
{
kf <- .kalman(y=as.numeric(y[t,,drop=FALSE]),x=x.mod[t,,drop=FALSE],
theta=theta.av[t,,drop=FALSE],E=R[[t]],
V=Vs[[i]],lambda=lambda,kappa=kappa,t=t+length(object$V))
}
else
{
kf <- .kalman2(y=as.numeric(y[t,,drop=FALSE]),x=x.mod[t,,drop=FALSE],
theta=theta.av[t,,drop=FALSE],R=R[[t]],
t=t+length(object$V),Rw=object$data.last[[9]],Vv=V)
}
y.pred[t,i] <- kf$y.hat
thetas[[i]][t+1,] <- kf$theta
Es[[i]] <- kf$E
Vs[[i]] <- kf$V
predpdfs[1,i] <- kf$pdens
}
w.bar <- (t(predpdfs) %*% w[t,,drop=FALSE]) * a
w.bar <- w.bar / sum(w.bar)
w[t+1,] <- rowSums(w.bar)
if (atype==0)
{
v <- v + w.bar
}
else
{
v <- mKIapprox(w.bar,v)
}
a.bar <- colSums(v)
for (i in 1:ncol(v))
{
a[,i] <- v[,i] / a.bar[i]
}
thetas.mods <- matrix(0,nrow=nc,ncol=ncol(x))
for (i in 1:nc)
{
thetas.mods[i,] <- thetas[[i]][t+1,]
}
theta.av[t+1,] <- w[t+1,,drop=FALSE] %*% thetas.mods
R.temp <- matrix(0,nrow=ncol(x),ncol=ncol(x))
for (i in 1:nc)
{
e.theta <- theta.av[t+1,,drop=FALSE] - thetas[[i]][t+1,,drop=FALSE]
R.temp <- R.temp + as.numeric(w[t+1,i]) * Es[[i]] + as.numeric(w[t+1,i]) * (t(e.theta) %*% e.theta)
}
R[[t+1]] <- R.temp
if (ftype==0 || ftype==1)
{
theta.out[t+1,] <- theta.av[t+1,]
R.out[t+1,] <- diag(R[[t+1]])
V.temp <- 0
for (i in 1:nc)
{
V.temp <- V.temp + w[t+1,i] * Vs[[i]]
}
V.out[t+1,] <- V.temp
}
if (ftype==2)
{
theta.out[t+1,] <- thetas.mods[which.max(w[t+1,]),]
R.out[t+1,] <- diag(Es[[which.max(w[t+1,])]])
V.out[t+1,] <- Vs[[which.max(w[t+1,])]]
}
if (ftype==3)
{
j <- matrix(0,ncol=ncol(x),nrow=1)
j.var <- as.vector(w[t+1,] %*% mods)
j.var <- which(j.var >= 0.5)
j[1,j.var] <- 1
j.mod <- which(apply(mods,1,function(x) all(x == j[1,])))
theta.out[t+1,] <- thetas.mods[j.mod,]
R.out[t+1,] <- diag(Es[[j.mod]])
V.out[t+1,] <- Vs[[j.mod]]
}
if (t<T) { y[t+1,] <- rnorm(n=1,mean=as.numeric(x[t+1,,drop=FALSE] %*% t(theta.out[t+1,,drop=FALSE])),sd=(as.numeric(V.out[t+1,]))^0.5) }
}
for (i in 1:nc)
{
thetas[[i]] <- thetas[[i]][-nrow(thetas[[i]]),,drop=FALSE]
}
if (ftype==0)
{
y.hat <- as.numeric(diag(x %*% t(theta.av)))
pip <- w %*% mods
}
if (ftype==1)
{
y.hat <- as.numeric(diag(y.pred %*% t(w)))
pip <- w %*% mods
}
if (ftype==2)
{
y.hat <- vector()
pip <- matrix(0,nrow=nrow(w),ncol=ncol(x))
for (i in 1:nrow(w))
{
y.hat[i] <- y.pred[i,which.max(w[i,])]
pip[i,] <- mods[which.max(w[i,]),]
}
}
if (ftype==3)
{
y.hat <- vector()
pip <- matrix(0,nrow=nrow(w),ncol=ncol(x))
j <- matrix(0,ncol=ncol(x),nrow=nrow(w))
for (i in 1:nrow(w))
{
j.var <- as.vector(w[i,] %*% mods)
j.var <- which(j.var >= 0.5)
j[i,j.var] <- 1
j.mod <- which(apply(mods,1,function(x) all(x == j[i,])))
y.hat[i] <- y.pred[i,j.mod]
pip[i,] <- mods[j.mod,]
}
}
if (!ftype==0) { y.hat <- y.hat[-length(y.hat)] }
return(y.hat)
}
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