Rpart equivalent to LM's se.fit - r

Question

I have 2 regression models in R:
LM model in which I use the se.fit=true as follows:
predict(my_model, newdata=data, se.fit=T)
Recursive Portioning Tree (using rpart package)
Unfortunately I don't have the se.fit option in rpart, and I'd like to calculate these values manually.
I do understand what the standard error means for a group of estimations (basically sum of mean squares) but what does it mean per each estimate separately as generated by se.fit?
How can I do that? Thanks!

Answer 1

After digging into that, I found out LM's se.fit is calculated in a little odd way. here is the implementation:
function (object, newdata, se.fit = FALSE, scale = NULL, df = Inf,
interval = c("none", "confidence", "prediction"), level = 0.95,
type = c("response", "terms"), terms = NULL, na.action = na.pass,
pred.var = res.var/weights, weights = 1, ...)
{
tt <- terms(object)
if (!inherits(object, "lm"))
warning("calling predict.lm(<fake-lm-object>) ...")
if (missing(newdata) || is.null(newdata)) {
mm <- X <- model.matrix(object)
mmDone <- TRUE
offset <- object$offset
}
else {
Terms <- delete.response(tt)
m <- model.frame(Terms, newdata, na.action = na.action,
xlev = object$xlevels)
if (!is.null(cl <- attr(Terms, "dataClasses")))
.checkMFClasses(cl, m)
X <- model.matrix(Terms, m, contrasts.arg = object$contrasts)
offset <- rep(0, nrow(X))
if (!is.null(off.num <- attr(tt, "offset")))
for (i in off.num) offset <- offset + eval(attr(tt,
"variables")[[i + 1]], newdata)
if (!is.null(object$call$offset))
offset <- offset + eval(object$call$offset, newdata)
mmDone <- FALSE
}
n <- length(object$residuals)
p <- object$rank
p1 <- seq_len(p)
piv <- if (p)
qr.lm(object)$pivot[p1]
if (p < ncol(X) && !(missing(newdata) || is.null(newdata)))
warning("prediction from a rank-deficient fit may be misleading")
beta <- object$coefficients
predictor <- drop(X[, piv, drop = FALSE] %*% beta[piv])
if (!is.null(offset))
predictor <- predictor + offset
interval <- match.arg(interval)
if (interval == "prediction") {
if (missing(newdata))
warning("predictions on current data refer to _future_ responses\n")
if (missing(newdata) && missing(weights)) {
w <- weights.default(object)
if (!is.null(w)) {
weights <- w
warning("assuming prediction variance inversely proportional to weights used for fitting\n")
}
}
if (!missing(newdata) && missing(weights) && !is.null(object$weights) &&
missing(pred.var))
warning("Assuming constant prediction variance even though model fit is weighted\n")
if (inherits(weights, "formula")) {
if (length(weights) != 2L)
stop("'weights' as formula should be one-sided")
d <- if (missing(newdata) || is.null(newdata))
model.frame(object)
else newdata
weights <- eval(weights[[2L]], d, environment(weights))
}
}
type <- match.arg(type)
if (se.fit || interval != "none") {
w <- object$weights
res.var <- if (is.null(scale)) {
r <- object$residuals
rss <- sum(if (is.null(w)) r^2 else r^2 * w)
df <- object$df.residual
rss/df
}
else scale^2
if (type != "terms") {
if (p > 0) {
XRinv <- if (missing(newdata) && is.null(w))
qr.Q(qr.lm(object))[, p1, drop = FALSE]
else X[, piv] %*% qr.solve(qr.R(qr.lm(object))[p1,
p1])
ip <- drop(XRinv^2 %*% rep(res.var, p))
}
else ip <- rep(0, n)
}
}
if (type == "terms") {
if (!mmDone) {
mm <- model.matrix(object)
mmDone <- TRUE
}
aa <- attr(mm, "assign")
ll <- attr(tt, "term.labels")
hasintercept <- attr(tt, "intercept") > 0L
if (hasintercept)
ll <- c("(Intercept)", ll)
aaa <- factor(aa, labels = ll)
asgn <- split(order(aa), aaa)
if (hasintercept) {
asgn$"(Intercept)" <- NULL
if (!mmDone) {
mm <- model.matrix(object)
mmDone <- TRUE
}
avx <- colMeans(mm)
termsconst <- sum(avx[piv] * beta[piv])
}
nterms <- length(asgn)
if (nterms > 0) {
predictor <- matrix(ncol = nterms, nrow = NROW(X))
dimnames(predictor) <- list(rownames(X), names(asgn))
if (se.fit || interval != "none") {
ip <- matrix(ncol = nterms, nrow = NROW(X))
dimnames(ip) <- list(rownames(X), names(asgn))
Rinv <- qr.solve(qr.R(qr.lm(object))[p1, p1])
}
if (hasintercept)
X <- sweep(X, 2L, avx, check.margin = FALSE)
unpiv <- rep.int(0L, NCOL(X))
unpiv[piv] <- p1
for (i in seq.int(1L, nterms, length.out = nterms)) {
iipiv <- asgn[[i]]
ii <- unpiv[iipiv]
iipiv[ii == 0L] <- 0L
predictor[, i] <- if (any(iipiv > 0L))
X[, iipiv, drop = FALSE] %*% beta[iipiv]
else 0
if (se.fit || interval != "none")
ip[, i] <- if (any(iipiv > 0L))
as.matrix(X[, iipiv, drop = FALSE] %*% Rinv[ii,
, drop = FALSE])^2 %*% rep.int(res.var,
p)
else 0
}
if (!is.null(terms)) {
predictor <- predictor[, terms, drop = FALSE]
if (se.fit)
ip <- ip[, terms, drop = FALSE]
}
}
else {
predictor <- ip <- matrix(0, n, 0L)
}
attr(predictor, "constant") <- if (hasintercept)
termsconst
else 0
}
if (interval != "none") {
tfrac <- qt((1 - level)/2, df)
hwid <- tfrac * switch(interval, confidence = sqrt(ip),
prediction = sqrt(ip + pred.var))
if (type != "terms") {
predictor <- cbind(predictor, predictor + hwid %o%
c(1, -1))
colnames(predictor) <- c("fit", "lwr", "upr")
}
else {
if (!is.null(terms))
hwid <- hwid[, terms, drop = FALSE]
lwr <- predictor + hwid
upr <- predictor - hwid
}
}
if (se.fit || interval != "none") {
se <- sqrt(ip)
if (type == "terms" && !is.null(terms) && !se.fit)
se <- se[, terms, drop = FALSE]
}
if (missing(newdata) && !is.null(na.act <- object$na.action)) {
predictor <- napredict(na.act, predictor)
if (se.fit)
se <- napredict(na.act, se)
}
if (type == "terms" && interval != "none") {
if (missing(newdata) && !is.null(na.act)) {
lwr <- napredict(na.act, lwr)
upr <- napredict(na.act, upr)
}
list(fit = predictor, se.fit = se, lwr = lwr, upr = upr,
df = df, residual.scale = sqrt(res.var))
}
else if (se.fit)
list(fit = predictor, se.fit = se, df = df, residual.scale = sqrt(res.var))
else predictor
}