Nothing
###############################################################################################################################################
# fitted method for marssMLE objects; expected value of rhs minus error term
##############################################################################################################################################
fitted.marssMLE <- function(object, ...,
type = c("ytt1", "ytT", "xtT", "ytt", "xtt1"),
interval = c("none", "confidence", "prediction"),
level = 0.95,
output = c("data.frame", "matrix"),
fun.kf = c("MARSSkfas", "MARSSkfss")) {
type <- match.arg.exact(type)
output <- match.arg(output)
interval <- match.arg(interval)
conditioning <- substring(type, 3)
type <- substr(type, 1, 1)
# Allow user to force a particular KF function
if(!missing(fun.kf)) object[["fun.kf"]] <- match.arg(fun.kf)
MLEobj <- object
if (is.null(MLEobj[["par"]])) {
stop("fitted.marssMLE: The marssMLE object does not have the par element. Most likely the model has not been fit.", call. = FALSE)
}
if (MLEobj[["convergence"]] == 54) {
stop("fitted.marssMLE: MARSSkf (the Kalman filter/smoother) returns an error with the fitted model. Try MARSSinfo('optimerror54') for insight.", call. = FALSE)
}
if (interval != "none" && (!is.numeric(level) || length(level) != 1 || level > 1 || level < 0)) {
stop("fitted.marssMLE: level must be a single number between 0 and 1.", call. = FALSE)
}
alpha <- 1 - level
# need the model dims in marss form with c in U and d in A
model.dims <- attr(MLEobj[["marss"]], "model.dims")
TT <- model.dims[["x"]][2]
n <- model.dims[["y"]][1]
m <- model.dims[["x"]][1]
if (type == "y") {
if (conditioning == "T") hatxt <- MARSSkf(MLEobj)[["xtT"]]
if (conditioning == "t1") hatxt <- MARSSkf(MLEobj)[["xtt1"]]
if (conditioning == "t") hatxt <- MARSSkf(MLEobj)[["xtt"]]
if (interval != "none") {
if (conditioning == "T") hatVt <- MARSSkf(MLEobj)[["VtT"]]
if (conditioning == "t1") hatVt <- MARSSkf(MLEobj)[["Vtt1"]]
if (conditioning == "t") hatVt <- MARSSkf(MLEobj)[["Vtt"]]
}
Z.time.varying <- model.dims[["Z"]][3] != 1
A.time.varying <- model.dims[["A"]][3] != 1
R.time.varying <- model.dims[["R"]][3] != 1
H.time.varying <- model.dims[["H"]][3] != 1
val <- matrix(NA, n, TT)
rownames(val) <- attr(MLEobj$marss, "Y.names")
if (interval != "none") se <- val
Zt <- parmat(MLEobj, "Z", t = 1)$Z
At <- parmat(MLEobj, "A", t = 1)$A
Rt <- parmat(MLEobj, "R", t = 1)$R
Ht <- parmat(MLEobj, "H", t = 1)$H
Rt <- Ht %*% tcrossprod(Rt, Ht)
for (t in 1:TT) {
# parmat returns marss form
if (Z.time.varying) Zt <- parmat(MLEobj, "Z", t = t)$Z
if (A.time.varying) At <- parmat(MLEobj, "A", t = t)$A
val[, t] <- Zt %*% hatxt[, t, drop = FALSE] + At
if (interval == "confidence") {
se[, t] <- takediag(Zt %*% tcrossprod(hatVt[, , t], Zt))
}
if (interval == "prediction") {
if (R.time.varying) Rt <- parmat(MLEobj, "R", t = t)$R
if (H.time.varying) Ht <- parmat(MLEobj, "H", t = t)$H
if (R.time.varying | H.time.varying) Rt <- Ht %*% tcrossprod(Rt, Ht)
se[, t] <- takediag(Zt %*% tcrossprod(hatVt[, , t], Zt) + Rt)
}
}
# Set up output
if (output == "data.frame") {
data.names <- attr(MLEobj[["model"]], "Y.names")
data.dims <- attr(MLEobj[["model"]], "model.dims")[["y"]]
model.tsp <- attr(MLEobj[["model"]], "model.tsp")
nn <- data.dims[1]
TT <- data.dims[2]
ret <- data.frame(
.rownames = rep(data.names, each = TT),
t = rep(seq(model.tsp[1], model.tsp[2], 1 / model.tsp[3]), nn),
y = vec(t(MLEobj[["model"]]$data)),
stringsAsFactors = FALSE
)
}
}
if (type == "x") {
if (conditioning == "T") hatxt <- MARSSkf(MLEobj)[["xtT"]]
if (conditioning == "t1") hatxt <- MARSSkf(MLEobj)[["xtt"]]
if (interval != "none") {
if (conditioning == "T") hatVt <- MARSSkf(MLEobj)[["VtT"]]
if (conditioning == "t1") hatVt <- MARSSkf(MLEobj)[["Vtt"]]
}
B.time.varying <- model.dims[["B"]][3] != 1
U.time.varying <- model.dims[["U"]][3] != 1
Q.time.varying <- model.dims[["Q"]][3] != 1
G.time.varying <- model.dims[["G"]][3] != 1
val <- matrix(NA, m, TT)
rownames(val) <- attr(MLEobj[["marss"]], "X.names")
if (interval != "none") se <- val
x0 <- coef(MLEobj, type = "matrix")[["x0"]]
if (interval != "none") V0 <- coef(MLEobj, type = "matrix")[["V0"]]
Bt <- parmat(MLEobj, "B", t = 1)[["B"]]
Ut <- parmat(MLEobj, "U", t = 1)[["U"]]
Qt <- parmat(MLEobj, "Q", t = 1)[["Q"]]
Gt <- parmat(MLEobj, "G", t = 1)[["G"]]
Qt <- Gt %*% tcrossprod(Qt, Gt)
if (MLEobj$model$tinitx == 0) {
val[, 1] <- Bt %*% x0 + Ut
if (interval == "confidence") se[, 1] <- takediag(Bt %*% tcrossprod(V0, Bt))
if (interval == "prediction") se[, 1] <- takediag(Bt %*% tcrossprod(V0, Bt) + Qt)
}
if (MLEobj[["model"]][["tinitx"]] == 1) {
val[, 1] <- x0
if (interval != "none") se[, 1] <- takediag(V0)
}
for (t in 2:TT) {
if (B.time.varying) Bt <- parmat(MLEobj, "B", t = t)[["B"]]
if (U.time.varying) Ut <- parmat(MLEobj, "U", t = t)[["U"]]
val[, t] <- Bt %*% hatxt[, t - 1, drop = FALSE] + Ut
if (interval == "confidence") {
se[, t] <- takediag(Bt %*% tcrossprod(hatVt[, , t - 1], Bt))
}
if (interval == "prediction") {
if (Q.time.varying) Qt <- parmat(MLEobj, "Q", t = t)[["Q"]]
if (G.time.varying) Gt <- parmat(MLEobj, "G", t = t)[["G"]]
if (Q.time.varying | G.time.varying) Qt <- Gt %*% tcrossprod(Qt, Gt)
se[, t] <- takediag(Bt %*% tcrossprod(hatVt[, , t - 1], Bt) + Qt)
}
}
# Set up output
if (output == "data.frame") {
state.names <- attr(MLEobj[["model"]], "X.names")
state.dims <- attr(MLEobj[["model"]], "model.dims")[["x"]]
model.tsp <- attr(MLEobj[["model"]], "model.tsp")
mm <- state.dims[1]
TT <- state.dims[2]
ret <- data.frame(
.rownames = rep(state.names, each = TT),
t = rep(seq(model.tsp[1], model.tsp[2], 1 / model.tsp[3]), mm),
.x = vec(t(hatxt)),
stringsAsFactors = FALSE
)
}
}
if (interval == "none") {
if (output == "matrix") {
return(val)
}
retlist <- list(.fitted = val)
}
if (interval == "confidence") {
se[se < 0 & abs(se) < sqrt(.Machine$double.eps)] <- 0
se <- sqrt(se) # was not sqrt earlier
retlist <- list(
.fitted = val,
.se = se,
.conf.low = val + qnorm(alpha / 2) * se,
.conf.up = val + qnorm(1 - alpha / 2) * se
)
}
if (interval == "prediction") {
se[se < 0 & abs(se) < sqrt(.Machine$double.eps)] <- 0
se <- sqrt(se) # was not sqrt earlier
retlist <- list(
.fitted = val,
.sd = se,
.lwr = val + qnorm(alpha / 2) * se,
.upr = val + qnorm(1 - alpha / 2) * se
)
}
if (output == "matrix") {
return(retlist)
}
return(cbind(ret, as.data.frame(lapply(retlist, function(x) {
vec(t(x))
}))))
} # end of fitted.marssMLE
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