Saving huge model object to file - r

Question

Say you have a model object of class 'varrest' returned from a VAR() regression operation.
I want to save the model to a file, but not all data which was used to estimate the coefficients.
How can one just save the model specification wihtout the training data?
Because when I save the model it has a file size of over 1GB and therefore loading does take its time.
Can one save objects without some attributes?

Answer 1

The predict.varest function starts out with this code:
K <- object$K
p <- object$p
obs <- object$obs
type <- object$type
data.all <- object$datamat
ynames <- colnames(object$y)
You can then investigate how much pruning you might achieve:
data(Canada)
tcan <-
VAR(Canada, p = 2, type = "trend")
names(tcan)
# [1] "varresult" "datamat" "y" "type" "p"
# [6] "K" "obs" "totobs" "restrictions" "call"
object.size(tcan[c("K","p", "obs", "type", "datamat", "y")] )
#15080 bytes
object.size(tcan)
#252032 bytes
So the difference is substantial, but just saving those items is not sufficient because the next line in predict.varest is:
B <- Bcoef(object)
You will need to add that object to the list above and then construct a new predict-function that accepts something less than the large 'varresult' node of the model object. Also turned out that there was a downstream call to an internal function that needs to be stored. (You will need to decide in advance what interval you need for prediction.)
tsmall <- c( tcan[c("K","p", "obs", "type", "datamat", "y", "call")] )
tsmall[["Bco"]] <- Bcoef(tcan)
tsmall$sig.y <- vars:::.fecov(x = tcan, n.ahead = 10)
And the modified predict function will be:
sm.predict <- function (object, ..., n.ahead = 10, ci = 0.95, dumvar = NULL)
{
K <- object$K
p <- object$p
obs <- object$obs
type <- object$type
data.all <- object$datamat
ynames <- colnames(object$y)
n.ahead <- as.integer(n.ahead)
Z <- object$datamat[, -c(1:K)]
# This used to be a call to Bcoef(object)
B <- object$Bco
if (type == "const") {
Zdet <- matrix(rep(1, n.ahead), nrow = n.ahead, ncol = 1)
colnames(Zdet) <- "const"
}
else if (type == "trend") {
trdstart <- nrow(Z) + 1 + p
Zdet <- matrix(seq(trdstart, length = n.ahead), nrow = n.ahead,
ncol = 1)
colnames(Zdet) <- "trend"
}
else if (type == "both") {
trdstart <- nrow(Z) + 1 + p
Zdet <- matrix(c(rep(1, n.ahead), seq(trdstart, length = n.ahead)),
nrow = n.ahead, ncol = 2)
colnames(Zdet) <- c("const", "trend")
}
else if (type == "none") {
Zdet <- NULL
}
if (!is.null(eval(object$call$season))) {
season <- eval(object$call$season)
seas.names <- paste("sd", 1:(season - 1), sep = "")
cycle <- tail(data.all[, seas.names], season)
seasonal <- as.matrix(cycle, nrow = season, ncol = season -
1)
if (nrow(seasonal) >= n.ahead) {
seasonal <- as.matrix(cycle[1:n.ahead, ], nrow = n.ahead,
ncol = season - 1)
}
else {
while (nrow(seasonal) < n.ahead) {
seasonal <- rbind(seasonal, cycle)
}
seasonal <- seasonal[1:n.ahead, ]
}
rownames(seasonal) <- seq(nrow(data.all) + 1, length = n.ahead)
if (!is.null(Zdet)) {
Zdet <- as.matrix(cbind(Zdet, seasonal))
}
else {
Zdet <- as.matrix(seasonal)
}
}
if (!is.null(eval(object$call$exogen))) {
if (is.null(dumvar)) {
stop("\nNo matrix for dumvar supplied, but object varest contains exogenous variables.\n")
}
if (!all(colnames(dumvar) %in% colnames(data.all))) {
stop("\nColumn names of dumvar do not coincide with exogen.\n")
}
if (!identical(nrow(dumvar), n.ahead)) {
stop("\nRow number of dumvar is unequal to n.ahead.\n")
}
if (!is.null(Zdet)) {
Zdet <- as.matrix(cbind(Zdet, dumvar))
}
else {
Zdet <- as.matrix(dumvar)
}
}
Zy <- as.matrix(object$datamat[, 1:(K * (p + 1))])
yse <- matrix(NA, nrow = n.ahead, ncol = K)
# This used to be a call to vars:::.fecov
sig.y <- object$sig.y
for (i in 1:n.ahead) {
yse[i, ] <- sqrt(diag(sig.y[, , i]))
}
yse <- -1 * qnorm((1 - ci)/2) * yse
colnames(yse) <- paste(ci, "of", ynames)
forecast <- matrix(NA, ncol = K, nrow = n.ahead)
lasty <- c(Zy[nrow(Zy), ])
for (i in 1:n.ahead) {
lasty <- lasty[1:(K * p)]; print(lasty); print(B)
Z <- c(lasty, Zdet[i, ]) ;print(Z)
forecast[i, ] <- B %*% Z
temp <- forecast[i, ]
lasty <- c(temp, lasty)
}
colnames(forecast) <- paste(ynames, ".fcst", sep = "")
lower <- forecast - yse
colnames(lower) <- paste(ynames, ".lower", sep = "")
upper <- forecast + yse
colnames(upper) <- paste(ynames, ".upper", sep = "")
forecasts <- list()
for (i in 1:K) {
forecasts[[i]] <- cbind(forecast[, i], lower[, i], upper[,
i], yse[, i])
colnames(forecasts[[i]]) <- c("fcst", "lower", "upper",
"CI")
}
names(forecasts) <- ynames
result <- list(fcst = forecasts, endog = object$y, model = object,
exo.fcst = dumvar)
class(result) <- "varprd"
return(result)
}

Answer 2

Either
set the attributes you do not want to NULL, or
copy the parts you want to a new object, or
call the save() function with proper indexing.