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bruto <-
function (x, y, w = rep(1, n), wp = rep(1/np, np), dfmax, cost = 2,
maxit.select = 20, maxit.backfit = 20, thresh = 1e-04,
trace.bruto = FALSE, start.linear = TRUE, fit.object, ...)
{
this.call <- match.call()
y <- as.matrix(y)
x <- as.matrix(x)
np <- ncol(y)
d <- dim(x)
nq <- d[2]
n <- d[1]
xnames <- dimnames(x)[[2]]
if (!length(xnames))
xnames <- NULL
ynames <- dimnames(y)[[2]]
if (!length(ynames))
ynames <- NULL
storage.mode(x) <- "double"
storage.mode(y) <- "double"
storage.mode(w) <- "double"
storage.mode(wp) <- "double"
storage.mode(cost) <- "double"
if (missing(fit.object)) {
nknotl <- function(n) {
a1 <- log(50)/log(2)
a2 <- log(100)/log(2)
a3 <- log(140)/log(2)
a4 <- log(200)/log(2)
cx <- as.numeric(cut(n, c(0, 50, 200, 800, 3200)))
if (is.na(cx))
cx <- 5
floor(switch(cx, n, 2^(a1 + ((a2 - a1) * (n - 50))/150),
2^(a2 + ((a3 - a2) * (n - 200))/600), 2^(a3 +
((a4 - a3) * (n - 800))/2400), 200 + (n - 3200)^0.2) +
6)
}
check.range <- apply(x, 2, var)
if (any(check.range < .Machine$double.eps))
stop(paste("A column of x is constant;",
"do not include an intercept column"))
nkmax <- nknotl(n) - 4
coef <- matrix(double(nkmax * np * nq), ncol = nq)
ybar <- apply(y * w, 2, sum)/sum(w)
if (start.linear && (nq * cost > n))
start.linear <- FALSE
if (start.linear) {
start.fit <- polyreg(x, y, w)
eta <- fitted(start.fit)
coef[seq(from = 2, by = 2, length = np), ] <-
t(start.fit$coef)[, -1]
type <- as.integer(rep(2, nq))
df <- as.double(rep(1, nq))
}
else {
eta <- outer(rep(1, n), ybar)
type <- integer(nq)
df <- double(nq)
}
nk <- integer(nq)
knot <- matrix(double((nkmax + 4) * nq), ncol = nq)
Match <- matrix(integer(n * nq), ncol = nq)
nef <- integer(nq)
lambda <- double(nq)
xrange <- matrix(double(2 * nq), 2, nq)
df <- double(nq)
if (missing(dfmax))
dfmax <- (2 * nkmax)/3
if (length(dfmax) != nq)
dfmax <- rep(dfmax, length = nq)
if (cost > 0) {
TD <- (n - sum(df))/cost
TT <- dfmax > TD
if (any(TT))
dfmax[TT] <- TD
}
storage.mode(dfmax) <- "double"
}
else {
this.call <- fit.object$call
ybar <- fit.object$ybar
nkmax <- fit.object$nkmax
dfmax <- fit.object$dfmax
eta <- fit.object$fitted.values
if (is.null(eta))
eta <- predict(fit.object, x)
nk <- fit.object$nk
knot <- fit.object$knot
Match <- fit.object$Match
nef <- fit.object$nef
lambda <- fit.object$lambda
coef <- fit.object$coef
type <- unclass(fit.object$type)
xrange <- fit.object$xrange
maxit.select <- 0
maxit.backfit <- fit.object$nit[2]
df <- fit.object$df
}
maxit <- as.integer(c(maxit.select, maxit.backfit))
names(df) <- xnames
names(maxit) <- c("selection", "backfitting")
gcv.select <- if (maxit.select)
matrix(double(maxit.select * nq), nq, maxit.select)
else double(1)
gcv.backfit <- if (maxit.backfit)
matrix(double(maxit.backfit * nq), nq, maxit.backfit)
else double(1)
df.select <- if (maxit.select)
matrix(double(maxit.select * nq), nq, maxit.select)
else double(1)
names(lambda) <- xnames
fit <-
.Fortran("bruto",
x,
as.integer(n),
as.integer(nq),
y,
as.integer(np),
w,
knot = knot,
nkmax = as.integer(nkmax),
nk = nk,
wp,
Match = Match,
nef = nef,
dfmax = dfmax,
cost = cost,
lambda = lambda,
df = df,
coef = coef,
type = type,
xrange = xrange,
gcv.select = gcv.select,
gcv.backfit = gcv.backfit,
df.select = df.select,
maxit = maxit,
nit = maxit,
fitted.values = eta,
residuals = y - eta,
as.double(thresh),
double((2 * np + 2) * ((n + 1) + 1) + (2 * np + 16) *
(n + 1) + 2 * (n + 1) + np),
integer(n),
as.integer(trace.bruto),
PACKAGE = "mda")[c("knot", "nkmax", "nk", "Match",
"nef", "dfmax", "cost", "lambda", "df", "coef", "type",
"xrange", "gcv.select", "gcv.backfit", "df.select",
"maxit", "nit", "fitted.values", "residuals")]
if (TN <- fit$nit[1]) {
TT <- fit$gcv.select[, seq(TN), drop = FALSE]
dimnames(TT) <- list(xnames, NULL)
}
else TT <- NULL
fit$gcv.select <- TT
if (TN) {
TT <- fit$df.select[, seq(TN), drop = FALSE]
dimnames(TT) <- list(xnames, NULL)
}
else TT <- NULL
fit$df.select <- TT
if (TN <- fit$nit[2]) {
TT <- fit$gcv.backfit[, seq(TN), drop = FALSE]
dimnames(TT) <- list(xnames, NULL)
}
else TT <- NULL
fit$gcv.backfit <- TT
TT <- factor(fit$type, levels = 1:3, labels = c("excluded",
"linear", "smooth"))
names(TT) <- xnames
fit$type <- TT
fit$ybar <- ybar
fit$call <- this.call
structure(fit, class = "bruto")
}
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