Nothing
trend <-
function(series, order=1, lowess=FALSE, lowspan=.75, robust=TRUE,
col=c(4,6), ylab=NULL, ...){
if (NCOL(series) > 1) stop("univariate time series only")
if (length(col) < 2) col = rep(col, 2)
name = deparse(substitute(series))
if (is.null(ylab)) { ylab = name }
series = as.ts(series)
tspar = tsp(series)
if (lowess) {
fam = ifelse(robust, 'symmetric', 'gaussian')
y = c(series)
x = c(time(series))
lo = stats::predict(stats::loess(y ~ x, span=lowspan, family=fam), se=TRUE)
trnd = ts(lo$fit, start=tspar[1], frequency=tspar[3])
tsplot(series, col=col[1], ylab=ylab, ...)
lines(trnd, col=col[2], lwd=2)
L = lo$fit - qt(0.975, lo$df)*lo$se
U = lo$fit + qt(0.975, lo$df)*lo$se
xx = c(x, rev(x))
yy = c(L, rev(U))
polygon(xx, yy, border=8, col = gray(.6, alpha=.2) )
invisible(cbind(trnd, L, U))
} else {
x = as.vector(time(series))
u = stats::lm(series~ poly(x, order), na.action=NULL)
up = stats::predict(u, interval="confidence", level = 0.95)
upts = ts(up, start=tspar[1], frequency=tspar[3])
tsplot(series, col=col[1], ylab=ylab, ...)
trnd = upts[,1]
lines(trnd, col=col[2], lwd=2)
xx = c(x, rev(x))
yy = c(upts[,2], rev(upts[,3]))
polygon(xx, yy, border=8, col = gray(.6, alpha=.2) )
invisible(upts)
}
}
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